What's up trade hackers welcome to today's update today's Tuesday March 17th Doesn't really feel like a st. Patrick's Day to me usually there's some craziness going on in the city a lot of green beer but not today my friends everybody is hunkered down and So what's going on in the markets today? Well, maybe it's maybe it feels lucky for you We're actually getting a little bit of a bounce in the market about a 200-point range in the S&P today It seems pretty calm compared to the ten percent 11% swings that we've seen the last few days big day down here then a big bounce up Then a big down day and then just kind of a a muted day But still a huge range with 200 points in the S&P's now up 528 NASDAQ up over 330 rustle up over 40 We still got a little less than an hour to go in the market from the time of this recording Oil continues its slide lower down over five percent today Gold wild rides in gold as well big range ended up about up about three percent today But the big news is bonds bonds down almost four and a half percent right now You know if you look at bonds bonds obviously have an inverse correlation to interest rates and so The Fed came out last week and it is surprise Well, maybe not such a surprise but came out and did a a 50 basis point rate cut and then came out When was it Monday? Sunday night I think came out and said we're cutting the rates to zero So they are cutting the Fed fund rates to zero now Does that mean you can get your mortgage at zero percent loan? No, but the federal fund rate is the rate that banks can borrow at okay So you're still gonna be paying you know a couple percentage at least on your mortgage on a 30-year note or 15-year note But that does bring the longer term rates down So the Fed is trying to control the shorter term rates by lowering the Fed fund rates And then they're also buying all these additional Treasuries to try to pump up or try to bring down the longer dated kind of the 30-year duration type notes as well. So Typically when you're seeing especially surprise rate cuts with the Fed That's when you would see these spikes in in bonds, but 50 basis point cut here bond market tanked It popped up a little bit yesterday after the slash to zero when they cut an additional 50 basis points, but down considerably today, so You know just a wild market not a lot of Rationals you would typically think of it in the markets right now So you just got to continue like I say all the time stay small stay mechanical and be careful out there because things are Whipping around like crazy Nat gas down almost 5% today and then let's take a look at some of the other stocks With a big update in the S&P's and the and the rest of the indices today You would think that the majority of individual stocks would be up, you know We're up over 3% up over 4.5% in the Nasdaq But if you look at kind of these closely watched stocks, you know most of our green But there's certainly still a lot of red on the screen And so if we kind of take a look at these, you know apples up over 4% Amazon is up over 6% Boeing continues its slide down at its down over 7% You know, this is one that I've started to actually nibble on I got long here and then sold out for a quick profit overnight when it Bounced 15% nibbled yesterday nibbled again today getting long So and the reason is is, you know, I think the airlines are going to be one of the Industries that gets bailed out here. And so when that happens, I think we see a pretty significant pop in Boeing Alibaba up 4% Be I be up 9% Beyond meat banks are getting hammered today with with bonds down. You're seeing the banks get hammered It's the Delta Airlines. Of course airlines getting hammered again I'd be really really careful if you're short airlines though I think I think they're gonna be getting a bailout at some point which could create some significant upside for them See what's Disney down with all the parks closing.  So what did we do today? Well, we added a little bit of a Directional play in VXX. So this is volatility, you know volatility has obviously Skyrocketed with the down move in stocks And so we thought it was time to add a little bit of short volatility So we put on a short directional trade in VXX. We also Rolled down our Short Delta trade so as the market continues to roll to go down We're continuing to roll down our short Delta plays roll down our short Delta plays just to keep that Some short Delta in our portfolio now We're actually to a point where our portfolio is actually a little bit long after this huge move down Which I don't mind being after a massive move like this You know, you think at some point you're gonna get a little bit of balance But I do still have a significant amount of short positions on For that downside as well. So fairly fairly balanced portfolio at this point But we're just gonna keep on keeping on keep on keeping our position sizes small continue to add short premium as the as the implied volatility stays through the roof So that's what we're doing. Everybody have a good evening. We will talk to you tomorrow


Okay, everyone will just shortly make a start. If you do have any questions, please do feel free to put them into the chat. My colleague Agnetta will be able to answer them. We'll have time at the end as well for a Q&A. So if you have any further questions that haven't already been answered by the webinar or by Agnetta in the chat, then we'll have time at the end to also discuss anything that may not have been raised. So yeah, just before we get going, welcome to this webinar on, it's called the Smash Your Interview webinar. So for some of our courses, undergraduate courses, and for some postgraduate courses as well, you will have to take part in an interview as part of the application process. So we're hoping that this webinar today will be able to prepare you for that part of the application process and answer any questions that you might have regarding the application process, how to prepare for your interviews. And yeah, how does Smash your interviews. So yeah, just before we get started, my name's Harry. I'm a student recruitment assistant with the University of Greenwich, and I'll be hosting this webinar. Welcome. Thank you very much for coming. And my colleague, Agnetta, who's also works with me in student recruitment, and she'll be answering questions in the chat. So we do have a Q&A function if you'd like to ask a question. And Agnetta will hopefully be able to answer them for you. But you can also ask questions in the chat and Agnetta will be monitoring that throughout the webinar. So without further ado, we'll get going on this. Just some quick things to go through regarding Zoom. Please do not share any personal information in the chat functions, or to any of the individuals in the meeting. Please only use your first name if you're joining the meeting. And do not share any, as I said, personal information like your telephone number, any photos address, any inappropriate language. This is a webinar to discuss information relating to interviews, anything that is completely, you know, out of the ordinary, we will be able to flag that. Agnetta will be able to see what comes into the chat. So please do keep everything appropriate. Do not directly message myself, for example, as the host, because it will be distracting me from providing the webinar. So please do keep your messages to just in the chat. And Agnetta will try her best to get through everything she can. And as I should stress this webinar is being recorded. So hopefully later on we'll be able to release some of this information onto our website and you'll be able to watch this recording later on at your own, at your own pace and your own time. Okay, this is just to do how to remain anonymous and zoom. After clicking the joining link you've already in here already, but you can put in a different alternative name. So hopefully you would have been joining without your video at the moment. And also all of your audio would have been turned off for the, for the, for the webinar as it goes on. And as I said earlier, please do ask questions or type comments into the chat. Okay, so hopefully by the end of this webinar, we would have gone over to what to expect on the day of your interview. So please actively prepare for your interview. The key criteria for selection during the selection process and some tips on best performance. And then a Q&A right at the end. Okay. So what is the point of an interview. As I said at the beginning it's not every single one of our courses that require an interview as part of the application process. We do provide, we do have an interview as part of the application process for certain courses. So this is to make sure that we are having the most suitable applicants for the courses that we provide. And that each applicant is given a fair and equal chance in securing their place. We want to make sure that the process is transparent, and it will be conducted within legal requirements. And of course we want to make sure that the best and most suitable candidates are being selected. If for example you wanted to go into something like adult nursing, midwifery, things like this. These are very professional courses where you would have to undergo placements and working in a health setting, things like this. And make sure that we are risk assessing the candidates if they will be coming out as prepared to go into a very professional environment. And whether you'll be suitable, HCPC registrants, so that's for those that will be monitored and regulated by the Health and Care Professions Council. So basically by law, people must be registered with the HCPC in many of the professions in the UK. So for example, you want to study paramedic science and you want to become a paramedic, then the HCPC regulates the best practice and standards related to that particular health service. So make sure that if you're going into those roles, into those courses, you will be able to meet the requirements set out by the HCPC. Now of course that's just health courses related, there are other courses that require interviews, and we'll go into that into some detail throughout the webinar. Okay, so interview tips. So there is a kind of new interview process. Now not every single course uses this. So for example, some of the teacher training courses, they do have an interview but they don't necessarily use choosing book. In those cases it will be likely the faculty themselves to the education faculty will reach out to you when they require you to book an interview and come in to be selected. So the book system is a new system, and to be able to allow you to book an interview slot at a time that suits you. So this is for most undergraduate and postgraduate courses that do require an interview, or an audition as part of the application process so some of the, you know design courses for example, you would need to bring in like a portfolio and things like that and that will be discussed at your interview. But the benefit really is that the process allows yourselves to book either in person or an online, online interview. Okay, when it comes to some of the education courses for example, all of them will be taking place in person. They're not, there's not kind of choice to do them online. Now we have received a lot of inquiries about this so if you, if you have already applied for some of the courses that I've mentioned midwifery adult nursing children's nursing a lot of the health courses in particular. Then then we do say that you know you need to go on to the choosing book system and just keep on refreshing the page keep on checking for new interview slots interview times. They will be updated regularly, but we do put the onus on yourselves to just check that. It gives you a bit more power and being able to kind of choose a slot on time that works for you rather than, than the admissions team deciding that please do check those things through on our website. They will be updated regularly as I've said. So I'm preparing for the interview so where's a good place to start. Well of course I'm going to sound slightly biased and saying this but I would say the best place to start is the University of Greenwich website. Whatever the course is that you're interested in. That has an interview as part of the application process. The best place to start is the website. Read through what is expected of you read through the course content the module content, maybe even do some further reading around the subject that you might be interested in. So if it is for things as I said earlier like nursing midwifery paramedic science. These are professional programs right. And you get one chance to make a very good impression. And as part of this you will need to make sure that you are demonstrating particular skills and particular kind of characteristics I guess so you need to show that you're committed you need to show that you're motivated in this area. And you need to show that you can practically work what the course asks of you when it's full time that you will be able to do that, whether you will be able to attend the placements and pay fees and things like that this is all very important.  So you would need to of course make sure that you're dressing appropriately for these interviews, speaking formal language. And, you know, one day hopefully you will become a registered nurse midwife paramedic as I've said these are professional programs, moving into a sector that is very professional so you need to keep that in mind. So this is something that would be useful to ask yourself when you're thinking about the interview, why do you want to become a, and then what the course might be. You need to make sure that you're showing many of these features and characteristics as I've said earlier, an understanding of the field that you want to go into drive and passion, a caring nature if it's for certain health courses like nursing, where you might be working with very very young children, you know newborn children in the case of midwifery and things like this authenticity, enthusiasm and motivation. You need to make sure that you're translating your skills and experience to the course and to the program leaders and to whoever is will be interviewing you. How did past experiences for you translate into, you know, being able to manage what will be potentially quite stressful environment. You know, for example if you were going into a health setting like being a nurse or paramedic, it might be quite stressful environments you need to be able to explain how you'd be able to kind of manage these settings as well. Now I would really recommend trying out what's known as the NHS values quiz. This will allow you to kind of understand what the core values are within a kind of health profession. It doesn't take too long. It kind of asks you quite a few questions and works out roughly, so it might ask you do you like to look after children things like this then it will be able to map roughly where it thinks where would be best for you to work within a health setting for example. You can also also look on the National Career Service website. This includes real life stories from NHS staff. So give you a really great kind of insight into what goes on in the world of the NHS and things like that. Okay, so these are some of the things you might want to bring up in your interview, what you like what excites you about the particular course and role, what your core values are your skills set. Past educational experiences is really important and also past work experiences. I think this one is particularly important if you can already show that you've you know you demonstrate that you've got experience under your belt in the health setting let's say then that will be really taken upon by by the interviewers. And then personal experiences these things do do matter and they do count in as part of the interview. And if they are, if they can translate into to what the courses you'd like to do they they can't necessarily be completely abstract, they do need to be connected but they can definitely be used and will be helpful for for the interview. So I wanted to prepare. So as I said earlier, good place to start is our website. Reading through revisiting going through the course page, checking through what the kind of modules are for the course. If you wanted to go into educational health courses, then you should make sure that you check all the mandatory checks that will be acquired identification that you will need. So you will need to bring certain things along to the interview. And if you're a candidate for some of the design courses as I said animation design architecture, you will need to bring a portfolio to the interview day, and then that will be discussed as part of the interview. Okay, but you might be asking, well, what is a portfolio. It's a creative piece of work. It's a body of work that will show off your kind of imagination, your current skills, ideas, and many other things as well. Of course, we, you know, we don't expect that you would have already mastered animation of already mastered, you know design and things like that. If you can show that you've already got a passion in that field, and that will really translate well when you're explaining your portfolio during the interview. You might show sketches, drawings, photography, craft work, 3D work, whatever it is that is suitable for the course that you've applied for. Generally, it will contain physical, it can contain these things for just a physical digital interactive work or screen based work. So if it's animation, for example, you might want to show more screen based work. If it's an architectural course, you might want to show more physical or written work or sketches and things like that. Okay, if you are going for one of these design courses, then you need to make sure you bring your portfolio with you on your interview day. If you are based outside of England and have been asked to send that in, then you used to do so at least three days before your interview date, and you would email a design admissions team. So let's just design underscore admissions at Greenwich.ac.uk. Online interviews are only available for those who do not live in England though, so if you are, you do reside in England, then you would be expected to come in person for these interviews. And how you would need to save it. So you can see the format there. So for example, if you were John Smith and you were doing animation, that would be the kind of portfolio, how you'd need to save it. Include your name, your UCAS ID, your applicant ID, the course you're applying for, and then you have the opportunity to discuss this during the interview. Okay, so some of the things that you would want to include in the portfolio. Now these aren't necessarily just the only things, but these are things that would be really suitable for the portfolio. So drawings, as I said earlier, any sketches that show your kind of passion for the field that you'd like to go into photography. Of course, you know, the eye is very important when it comes to architecture. Whether it comes to making and designing things, you might want to bring in a 3D artifact that you've made a model, metalwork, woodwork, even clothing potentially. If it's design technology, then your portfolio would not just consist of those things. So you might have to bring in other things as well. And then disciplinary skills. So this depends on the specific course you're applying for. But it includes what that shows some sort of potential for developing your chosen area. So it would be really fantastic if you came to your interview and you'd shown that you'd thought outside of the box. So potentially a new concept if it's something architectural, if it's something design related animation related and maybe you found an area that's not been touched upon before. That would really show that you've gone kind of above and beyond already. And the interviews are really, really like take to that. Okay, so this is just specific guidance depending on your course. So as I said, depending on these courses, then you need to make sure you prepare your portfolio, according to the instructions set out for your course. So you can find a lot of this information on the website. You should also be given some of this information as well. Okay, so before your interview, there are a few things you need to make sure that you're doing. So obviously this depends on whether your interview is online or in person. So if you're online, you should you should receive an email invite before your interview. And it will be a link for you to join a kind of Microsoft Teams meeting. Before you settle with that, I would definitely recommend giving yourself a lot of time before this to make sure if you've got internet connection issues, whatever it is, it might be that the teams meeting takes a while to load make sure you give yourself plenty of time. So if you're using a PC or laptop for the online experience, it's really, you know, not kind of a good image if you turn up trying to do an interview and you're on your phone or something. It just doesn't doesn't work as well and it doesn't look as professional and it doesn't look like you're taking the interview as as seriously. So if you want to make sure that you you do use a PC or desktop or laptop. Make sure you are in a quiet place, you don't want to be doing an interview in a noisy cafe or in, you know, in a public park where there's people running around you or whatever it is need to make sure you're in a quiet place. So again, you don't want the kind of background to be, you know, you in in a shop or something like that. You need to make sure that you're in preferably your home or a quiet part of a library or something like that and make sure you're in a quiet setting. And as I said, make sure you give plenty of time to set up your device login and prepare if you think.  But that's why if you take 1015 minutes make sure you've got water with you as well. And just have a kind of 1050 minutes to just, you know, breathe and get ready for the interview. Make sure all of your sound and video work, you don't want to start the interview and realize that your headset doesn't actually work because no one's going to be able to hear you. So if that is is fully prepared and of course be well dressed and comfortable. You know, I can't stress this enough, although it is an online interview, you will still be expected to, to look professional. So dress dress just as you would if you were turning up to an in person interview. Now on top of that, I would definitely recommend making sure you're in an area with good lighting. This would make sure that your picture is clear. You know, the interview want to make sure that they can see your facial expressions because that can also show them quite a lot about who you are as a person. If you're smiling and how you're responding to questions is really, really useful for the for the interviewers. And try to make sure that the lighting is in the correct kind of direction, so that you do not just appear with like a shadow of your face or whatever it is. And make sure that you are looking into the camera so just as you were in an in person interview, you would be trying to keep eye contact. You want to make sure you're doing the same thing in an online interview as well. On top of that, if you were to be doing this in person, it will be a similar set of things you need to make sure you prepare. But in this would be slightly different in the fact that the email invite will go into your email inbox. Sometimes we get applicants that they can't find their invites and make sure you check all of your spam and your junk just in case. Before you arrived, if you're attending one of the health courses related interviews and it will all be at Avery Hill campus, and a lot of the and as well a lot of the, you know, education courses as well. So those will take place at Avery Hill campus in the Mary Seacole building. So I would actually really recommend if you know exactly when your interview date is maybe come and have a look at the site a few days before or even a week before. Familiarize yourself with where the buildings are because you don't want to turn up with, you know, 10 minutes to spare and you don't know where Mary Seacole is or you don't know your way around. Of course there will be you know security around can help you but just make sure you give yourself plenty of time in preparing for for the interview. If it's in person, taking into account whether it's, you know, travel disruptions train strikes things like that take all of that into account when you are going for your in person interview. As I said make sure you turn up with plenty of time to spare. If you have official documents for inspection at your interview, I'll go into more detail on this in a minute and prepare yourself for what will be a panel interview, so you will be answering questions, but to multiple panelists. So one just be one to one, it'll be one to maybe two or more people okay. Bring a bottle of water. And again make sure you're well dressed and comfortable. As I said you will need to bring in a few things for the actual interview process you will need to present certain documents as part of the interview process. So your passport or driving license, of course if you're going into something like paramedic science, you want to become a paramedic, then you will need to of course have driving license. So these are the things as well so if you're a home applicant, UK born need to make sure you bring a current valid passport or UK driving license. And others also apply. And I should say if you didn't know already, the majority of our health courses are not open to international students as a result of visa requirements. So if you are, say an EU student, you need to make sure you have settled status. We can go into more detail in this maybe in the Q&A section if you have questions or, or, or you're not sure how exactly that process works. I would also say, when you're doing that research on our website. If you scroll right to the bottom and it will usually have a section about whether the course is open to overseas applicants. But again, if if you can't find that or you're not entirely sure exactly which courses are open to international students or not, then please do feel free to get in touch with us or go through our contact details at the end if you don't already know how to get in touch with UK student recruitment. Okay. So as I said, you need to bring an ID that must be valid in needs to be in date. And an original document with proof of your legal name. So to ensure that you actually meet their professional requirements of the course. We might take a screen screenshot. So this might be for example if you're doing an online interview that need to take a screenshot run authentication. It's not stored against your record. And that's held during appropriate kind of early period retention period. And then please make sure you have correct documentation prior to interview. And if you're unable to provide that, then the interview and I'll be able to continue with your interview. Okay, so this is really important. You really need to make sure that you're turning up with all of the documentation required. So this is just additional information depending on the course that you've applied for if it's be a social work be a primary. You must also present your letter of invitation evidence of a change of name so if you've got marriage certificate deed poll if applicable educational certificates to make sure you bring them or have copies of them that you can bring to the and a passport size photograph again for your record. So this is what it comes back to what I was saying earlier on at the webinar that these these are a lot of these are very professional programs right a lot of some of the design courses they're accredited with very very kind of well renowned institutions. Right likes like Reba for example the Royal Institution for which architecture is a really renowned institutions, whether it's with Miff, with miffery nursing paramedics paramedic science. You know these are courses that will hopefully set you up to move into a help setting in the NHS, potentially in the private sector depending on what you've what you've applied for. Okay, so you need to make sure that you've prepared yourself with all of this documentation. If you're not able to bring these things or you forget them or whatever that whatever happens, then of course the interviewers aren't going to look at that as someone who is passionate is motivated and is determined with this course. Okay, so please don't do do take that into account make sure that you, you bring all of this documentation with you. Now, of course I'm not expecting that you would remember literally everything that I've said that you need to bring. But you can find a lot of this information on the website as well. Okay, I'm just going to double check the chat so the chat is is okay at the moment. So please do feel free to use the chat. If you, if you need to find some information, Agnetta will be free to to find some of the links maybe for you. So feel free to use it. If it's a specific course, then again Agnetta will be able to help you with that. Okay, so during the interview. Let's focus on the question. Okay, a lot of people when they prepare for interviews, they turn up with a preset view of what how the interview is going to go. Although probably ask me these questions so I'll prepare as if they're going to ask those questions. I would say that's a common mistake and will come on to common mistakes in a minute. Prepare, as I've said, read around the subject. Do further reading on the NHS website as a health course. Do further reading maybe for example if it's architecture. Read about the Reba read about the Royal Institution for British architecture. Do further reading focus on the question so do not prepare with a question in mind prepare for whatever question might be asked. Answer what is asked. Okay. So do not, you know, turn up thinking what it's a nursing course or it's midwifery course so they're going to ask this, they might not. They might ask something very personal related, you know, not, you know, delving in deep but they might ask something to kind of judge your character. These are things that you also need to prepare. So, you know, don't don't turn up and say I've got no experience or something like that, for example, turn up and with with as much evidence of your strengths possible. If you are someone who for example hasn't got a lot of work experience under your belt, then you can still show all I've been proactive in looking for this kind of work experience. I've started volunteering I've started doing this this will show that you are motivated. Do not be afraid to ask for clarification or to come back to a question. Okay, I think some people panic that when a question's been asked they have to. Oh, I've got to find the answer immediately. Okay, we're all human we understand that people are under stress and and there's a lot of a pressure pressure around interviews. I do take your time asked for clarification if you if you're not 100% sure if the if the question is making sense. Then they do feel free to do that and they will respect that a lot more rather than trying to answer it kind of frantically okay. And have some sensible questions ready for the panel. Okay, if you come having done research. Maybe you've read an online kind of article around the area maybe you've read something specific about children's nursing. And you say well I was reading this the other day about children's nursing. Is that kind of the work that I might be doing or you know make sure that you've come prepared questions. Because again that will really show that you're, you've got the motivation you've got the passion there already. And of course, smile and be nice. Okay, so, you know if think of for example if you're going into healthcare. If you were going to the hospital you'd want to be treated respectfully you want to be treated by a nurse who is very nice and is looking out for your best interests. So that's something that they'll be looking for when it comes to the interview stage for the for the course. Okay. So for more information as I said we do have a few links that you can check out on interview preparation. So please do if you need to screenshot that or whatever feel free. You can just literally go to our main website gre.ac.uk and then just go to how to prepare interviews okay. So I've gone through the main things you need to make sure that you, you do bring to the interview in terms of documentation I've gone through what you need to do during the interview. So I'm also going to give a few tips on common mistakes things that you should not do during the interview. Okay, so make sure you've come prepared if you've come with not enough preparation. The interviewers, the academics for example they will be able to see that they'll know if you've maybe not read around an area, especially if they say what what area within children's nursing you interested in and you're not sure. You know that they might be able to see that you're not particularly decisive around particular things. Okay, so make sure you have prepared. Please make sure as I said, right at the, you know, earlier on, try not to be late, try not to look disorganized these are things that again will not translate well when it comes to the course itself. And also to to the environment of a health setting. Okay, if you if you're late to an interview, there's something for something that is quite a serious profession professional program as I've said, then the academics might say well if they're going to go into an NHS setting, or they're going to go into professional setting in teaching or in design animation things like this. It's not, it's not particularly good characteristic to have. Okay. So you need to be punctual. And this is the reason why at the beginning I stressed, try to try to give yourself as much time as possible. Try to plan your journey, you know, not everyone lives near every whole campus and I don't think any of us would expect that you would just live around the corner. But make sure you plan your route, check it a week before, maybe test the route, make sure that okay so I've tested it it takes me about an hour without traffic things like that test all these things in advance. So make sure you know a few days before and the day before that you're still checking that the route is all fine. Okay, because we know that they might be trained disruptions and things like that. Take all of that into account. Now of course that is when it comes to in person, but it goes the same when it comes to being online. Make sure that you give yourself plenty of time to get ready. Get dressed all of that kind of stuff. Okay. So that answers right so if if there is an open question. Right so the question that doesn't just expect you to say yes or no, something that expects you to go into more detail. Then a common mistake is to give maybe like a one sentence answer. It doesn't necessarily show that you have the kind of passion and the care for that course. Okay, and that's what the, the admissions tutors and the academics will be looking for. They'll be looking for someone who is is genuinely really interested as shown so much passion has done further reading has proven that they've got some work experience and personal experiences. Let's say for example you've not got work experience. Some applicants think I've not got five years and as a health assistant or something like that. Then then use a personal experience.  Okay, these are examples of you with a caring nature with compassion. So take those things into account as well. And on top of that, long rambling answers so there's a there's a kind of middle ground you need to find an in between something that's not, you know, a kind of five second answer, but also not 10 minute, a 10 minute answer is to be concise, work out, you know, maybe two or three really good examples for example and kind and try to keep to them. Of course you don't have to not just limited to that. But try to keep things as concise to the point, use the best examples to answer the question. So you might think well I've got these three examples, but use the best one that will make sure that it fully answers the question. So if you've dealt with a scenario how you've overcome a challenge. These are things that will also be that we really looked upon in in a positive way. And over rehearse questions answers sorry so I said this again right at the beginning.  So you're not going to have answered the question. And it shows a kind of lack of flexibility. It shows that you've, you've not maybe thought outside the box, and things like that so to make sure that you, as I've said, read as much as you can around the subject. So try not to turn up having read, you know, maybe only a few a few things and you're only using those as your things to talk about okay so try not to over rehearse things. There is a certain kind of flexibility that's expected as I said. And again, what I mentioned earlier, not answering the question. So make sure you really focus us for clarification. And then they'll, they'll be happy to help you maybe reword the question for you and things like that. Nervous self sabotage so this is a really important one. Of course it is tricky, you know, not everyone is you know immediately. You know used to just doing interviews or used to being in an interview environment. What you need to do is to turn up kind of preparing to fail with that as your mindset. You need to make sure that you've got a positive outlet. Okay. And, and make sure that you do, you know, find a way maybe if you're interviews in a few weeks find a way that you can help manage your nerves test ways that might be able to help you de stress before before something before a particular and then you can get into the swing and more of a routine of being able to deal with those environments. Okay. Okay, so what happens next. So, you get a conditional offer made by UCAS. And then you need to meet your conditions by the end of August. Depending on the course you will have to do a DBS check and an occupational health assessment. So your DBS if you've already got one for example then that will be another thing that will need to be checked in terms of whether it's in date. It needs to be in date and of course it depends on what level your DBS is at, if you've only got one that allows you to, you know, unless it doesn't allow you to work with children then you would need to kind of get an updated DBS and all of that will be done as part of the application process. And if you're unsuccessful then you can ask for feedback when you interview. Okay, so to do that you would need to get in touch with us, ask for feedback. If you're unable to help you out. If you're unsuccessful and you want to know more about your kind of application then you would get in touch with decisions at GRE.ac.uk. Okay, next steps right. So, we've got a few things coming up. We've got a few webinars that I think you'll, you'll hopefully enjoy. We've got a postgraduate open evening coming up on the 5th of April. So definitely sign up to that if you're interested in postgraduate study. And then definitely go on to the webinar part of our website. And you can find all of the upcoming events on there. I really recommend signing up to those as well. And let me just double check. So some of the things that you might have actually missed. We had an open day recently. So you might have just missed that. But I'll just go through. Follow us on all of our social media. And so our Facebook on our Twitter, Instagram and on TikTok. And then as I said earlier at the beginning, if you wanted to contact us, then it will be the UK student recruitment team or the course inquiries team. You can give us a call on<PHONE_NUMBER> or email course info at GRE.ac.uk. Okay, so we've got about 20 minutes or so for questions and answers. So I'll just go through to this slide for now. And I'll just stop the recording for now. But if you have any questions that haven't been answered, please put them in the.


So that's always a good time My name is Catherine Jarmel. I'm known across the internet as K jam I run a company called K Jamis and so you can find me blogging and writing and talking about things at kjamis and calm And I like working with text and data And if you're ever in Berlin come by the pie data meetup group We're a fun group of people and we'd love to meet you So I feel like because I'm talking on machine learning and I don't have a PhD next to my name I need to have this this disclaimer slide, which is I am not an expert in machine learning I can't sit here and tell you about which algorithm Algorithms work better and exactly why I'm a Python developer and I'm interested in applying machine learning to text analysis and sentiment analysis So by all means if you are a machine learning expert, and I say something wrong Feel free to either correct me in the questions or I will buy you a beer later, and you can teach me something So here's some assumptions. I've made about you. I'm not going to be going over NLP basics I'm not going to be going over machine learning basics I assume that we already have those covered if you want to talk about basics later than again, of course Happy to talk about it later, but I'm assuming that you already have done Maybe even a little bit of sentiment analysis But you already have played around with some natural language processing and you have some basic understandings of machine learning and what methods to use So here's a little bit of what we'll cover in the initial description I said that there was going to be live coding and demos in this and that Upon further inspection that becomes a little bit more difficult when we go into deep sentiment analysis and cross language Sentiment analysis, so what I'm going to do is I'm going to present you some code and some repositories that are good to use But we don't have time today for live coding instead. We're going to cover some of the tools We're going to talk a little bit about Watson and how it's being used We're going to talk a little bit about sentiment analysis and we're going to cover a lot of the research that's being done right now I'm seeing a massive gap in terms of the research that's being done around sentiment analysis and the tools We have available in code and I'm kind of hoping to push forward the Conversation in the Python community by giving this talk and kind of covering a little bit of what's happening So what we won't cover is some magical Python library where you can download today and it works in multiple languages for sentiment analysis I must say that if you want to take a look Please send hate mail to Salesforce So in case you wanted to send hate mail somewhere or you want to practice a natural language generation and send hate mail somewhere Send it to Salesforce MetaMind was a really amazing startup using matrix vectorization recursive or sorry yeah recurrent neural tensor networks and they had an open API and it was fabulous and then Salesforce bought them and they shut down the API and they're currently shutting down the paid API as we speak I ran into a guy who works for Salesforce who actually gets to work with this team and I asked him I baked him Please let something be publicly available and he said good luck. Have fun with that so Again all the hate mail and if you work for Salesforce and you can somehow get me inside the internal network that would be great So another one that's trying to do some things and they're one of the only ones that's available via apis is monkey learn And so here you can see that they have these different modules available They have some that are being actively worked on you can also create a publicly available module here So if you absolutely need hey K jam like I need something that works tomorrow I would start at some of these places, but we can see just even looking through the API That we have very different precision levels and so I would say That's why I'm giving this talk is we're gonna talk about some of the methods and the theory behind How to create a sentiment analysis that works for you So to begin with we're gonna talk about what is sentiment analysis and how do we go about it in Python? So what is sentiment analysis really and when I look at this tweet and I Being American some of my tweets in my Twitter feed are very American references So forgive me for being that way, but when we look at this tweet We have a lot of different sentiment right if we look at the emoji we see crying if we look at the Photo that we have here, then we see a different emotion of crying if we look at the text Then we might just see the word like and determine that this is a positive analysis And so when we're talking about sentiment analysis, we have all these different things We have stance. How does the author feel towards Jennifer Hudson? How does the author feel towards the BT awards? These are really complex things and we think we can boil them down into positive negative neutral But I am here to debunk that myth today So sentiment analysis is used in all different sorts of systems And I've had the pleasure of going to a sentiment analysis symposium recently and hearing just how many different places it's used There are even people using it in anomaly detection and they're charting sentiment across reviews or Twitter And they're looking at it via release tags and so there's all different ways that people are using sentiment analysis Including in obviously just simple user satisfaction or brand engagement So when we look at the sentiment analysis steps, there's can basically be broken down into four major steps and They basically go from left to right and top to bottom However, depending on what you choose they could be mixed up. So the first thing is dealing with your corpora Are you building a lexicon? What how are you using your labeling? Next then you're probably going to choose your algorithm or you're going to determine what model or approach to use Then you're doing parsing and pre-processing you might even do normalization and standardization across your data set and Finally, you're testing evaluating and improving and in that improvement step. You may revisit any of these earlier steps It's quite possible that you will need to to improve your model So we're going to go through the talk and we're going to basically cover all of the steps And we're going to talk about what's happening in research and what's happening in code The first step is choosing your lexicon So one of the oldest and most tested and true ways of autotagging a lexicon Particularly if you're using social media data is autotagging and it's this idea of distance supervision So I'm going to go and I'm going to gather tweets or I'm going to go and gather Facebook statuses or whatever I'm going to use and I'm going to map with emoticons or emoji and you can think about this also as a good Opportunity to use at the SARS so in quite a lot of the research. They will also just expand The corpora that they're using by using at the SARS So I want to grab all hashtag happy hashtag joyful etc. And this can also help expand Into new languages if you're working in one language and then you want to apply a different model as long as you have a very good The SARS then you can do that Then one of the other steps that you're going to need so now you have all of this data How do you tag it? We have this very naive idea that everything can be tagged positive negative or positive negative neutral or even positive negative indeterminate neutral that's Really in research being proven wrong and so what we have to think about here is if you really need a simple model That's simply positive negative and neutral Then you're you're going to have a lot of gray area And you're going to have probably more ending up in neutral or more ending up in false positives or false negatives So when you start moving away from that and you start thinking about Positive negative as a scale you start to see that things converge a lot faster and that you have less of these false positives and negatives This also means that you have to build your own lexicon and you have to label it yourself So there's pluses and minuses and depending on the resources of your team. You may not be able to do that There's also quite a lot of people looking at stance detection so this is a mixture of NLP topic identification or Entity detection and then using stance and a lot of that right now is essentially a bag of words models still and People are trying to evolve that to be more advanced and to try and look at things and say okay Can I determine that these set of words are actually applied to this? Organization or this entity and then I can start to detect these stance. So it might be positive towards I really like this restaurant. It has great food, but the service was total crap Then that means that I have one idea of the restaurant one idea of the food and one idea of the service Right and then finally we have this categorization by emotion because we have this wide range of emotions It's really difficult to determine just positive or negative So in terms of tagging a lexicon and it looks like one of my emojis is broken We have a few different methods So I'll actually start from the bottom the simple Boolean is zero one right or maybe negative one zero one Then we have this sliding scale which is going from completely negative to completely positive And I'm asking people then to just rate on this scale what they've actually found with research on that is that people tire over time So if you're sitting there asking me to rate something for five hours I'm eventually gonna just be like neutral neutral neutral and try to get done with it Right and like we know that just from our knowledge of humans. They also have found that Psychologically people avoid the edges. So you'll get much more representative of like it's kind of negative It's kind of positive because people don't want to say it's completely negative or completely positive So one of the best methods that's recently come into practice is this best worst scenario And you have a list of say four or maybe five words or n grams And you can use that and you can ask people say okay Choose the most positive and the most negative and that actually is creating some really interesting Lexicon and what they found is that people agree after about three examples So I can show it to three people and move on to my next sample and I'm in the 90% agreement range Which is pretty massive for tagging lexicons And this is another simple study and this is somebody Saif M. Muhammad who works in the National Resource Council in Canada does quite a lot of different talks on Semival competitions, which is like a sentiment evaluation machine learning competition that happens every year and He has some really great state-of-the-art models What he found is that when he switched to using a scale of negative one-to-one that That 90% of people agree within point four of each other So that was a really interesting revelation to move away from this zero one because we hear constantly Well humans don't even agree 77% of the time or whatever Statistics you look at and what he was able to find is when you give people a scale and you let people Speak on a scale you can actually find this least perceptible difference and particularly then if you even focus on native language Speakers you get an even smaller a smaller score difference there So I had the opportunity to chat and I'll hopefully be posting our chat with William Hamilton Who's currently working at Stanford on his PhD and he's developed a new model called social scent and what he's done is he's taken subreddits and he's Determined that subreddits and different communities use language differently So when I'm hanging out in our sports and I say oh man, he's really soft I don't mean that as a compliment right? I mean that in a really negative way So again, we have this mentality a lot of times in this code that we all use the same words to describe positive and negative And that's just not the case especially if you're doing sentiment analysis on a community and especially a community Like for example us as Python developers. We're gonna have a lot of different ways that we use words He has all of his data available and I took a look at our programming and I pulled out someone the most positive and the most negative I Also found out that Python has a slightly negative definition So I know you're all talking trash about Python on our programming and you need to stop So here I found some interesting things it's funny to me that 200s right in the middle like we can tell that we have web developers in our programming and Spaghetti is really negative, but I love spaghetti But spaghetti code maybe not so much and we find that Minecraft is has a great positive Association and so it's a really interesting data data set. It's only unigrams right now, but he's very open to suggestions So feel free to send him any suggestions or ask him to do a certain n-gram for you So now that we have our lexicon or we've chosen a lexicon we need to choose an approach or an algorithm so how do we do that what machine learning systems can we use and it turns out we can use a whole bunch and I know this is throwing a little bit of a lot on the screen But there are all different ways that people are using machine learning with sentiment analysis And they're achieving really great results with really small data sets So I'll come to this again later, but if you are dealing with movie reviews You're good to go But if you're not using the IMDb model or lexicon You start to enter this space where there's quite a lot of active research and not a lot of agreement on what's good to use Why or why not and people are achieving very different results with very different systems Obviously the finely tuned state of the art systems are still the best performing ones right now And this is these people that spend all of their time Researching and pulling out little details, but we don't have time for that right? We're probably Python developers first and sentiment analysis researchers next so I'll talk a little bit about how these are comparing First I'm going to talk about kind of the older approaches and the newer approaches The old approach used to be bag of words or even continuous bag of words and I would say oh, yeah It has mainly positive words. It must be positive The new idea is to start to use word embeddings and start to be able to use those word embeddings in deep learning models And by doing so I have a little bit of more complexity and I can say hey There are these words bunched together. Maybe they are bunched together because they have a sentiment together That's not always the case The old way was term frequency inverse document frequency, which is basically a nice way of doing bag of words on a document and The new way is using these doc to vet or these document embedding models The old way is kind of unigrams if I say a good job I mean good and I'm talking about job and they're all related and the new way is talking about skipgrams Or even dependency modifications, which is starting to look at the parts of speech and Label the words with parts of speech because I may mean very different things if I use a word as a noun versus a verb and The old way is these supervised state-of-the-art systems and the new way is kind of moving to a semi supervised approach So if you're curious about some of the state-of-the-art sentiment features and I'll post these slides because they reference the papers across the bottom This is a good summarization and this is a summarization of again site East Muhammad's model Which has done really well in competitions And these are all of the different things that he's pulling out as features and then he's fine tuning them and Analyzing them and and pulling out probably more sub features that we don't know about that haven't been released But you can see that there is a starting place if you're interested in tuning your own model But let's talk about what we can actually use as Python developers and what we can use easily We can use word to beck and doc to beck how many people in here have used Jensen Okay, so Jensen is this great library and what it gives you is these word embeds Sorry, how many people know about word to beck and doc to beck? Okay, super. So it's this idea of representing a word or a document as a vector So everything is in a vector space and it can be multi-dimensional It is multi-dimensional and What you're going to do is you're going to use Jensen and Jensen can load in these multi-dimensional vectors and give you a matrix or Vector representation of each of your words and your documents and what this is going to give you is a mathematical way to represent text Right and this is kind of where text was held back for quite some time Is we didn't have a really good mathematical way to represent text? We're using counters and bag of words and that's not that interesting So there's also glove Python. So there's two different ways to word embed Everybody talks really a lot about word to beck and I feel like there's not enough attention given to glove Glove is a Stanford model and it has a slightly different representation It uses a weighted model So word to beck is basically does this word appear in a sentence then it means it's somewhat related to these other words in the Sentence whereas glove is going to say hey these words are closer Therefore they might have more of a proximity in terms of meaning So depending on what word embedding you model you use or method you use you can get really different results So here we're comparing a word embeddings built on bag of words five, which is like a five n gram like a You know five words around count Then we have bag of words two and then we have a dependency which again is this part of speech And what we see is if you take a look at the Florida line What we see is in in Florida with bag of words we get this mixture of other ways to talk about Florida Cities in Florida, but when we move to dependencies we get other states and this is maybe Significant to whatever you're trying to do if you want other entities Likewise entities rather than just related words. You might want to look at a dependency word embedding rather than a bag of words word embedding There's an online comparison tool where you can look at this And so I took a look at Python and it looks like there's some of them mixed together If you use bag of words five two or the dependency you get slightly different results and the really neat thing about this is In this as well They give you a little bit of a visualization of how those word embeddings are used And so if you want to just play around and start to make decisions on what you're using for your word embeddings Then I recommend going to this site So another thing that I feel like needs to be raised is it was recently released There was a paper referenced at the bottom where word embeddings are not neutral They're based on human language and they have the same biases that you would expect to see in humans and To come up with some of these vectors. I was using the Google news Google news vectorization the 300 one and that one is used by places all over the place And I found some pretty atrocious stuff in there Maybe not in the first ten examples like for example here at the bottom There was some intense racism going on if I looked at the top 30 words that were within that space So I forewarned you to especially if you're using it for something like sentiment, right? Which were we may have some words mixed in there that are very biased and or if you're using it for natural language Generation that you need to be very aware that these biases exist one interesting thing However, is the authors of this paper were able to find they could find vectors Showing the actual like misogyny so they specifically focused on the misogyny in it And they were actually able to reverse it and reverse the wreck vectors by finding these vectors that were That were pushing things in a direction where like women had lesser professions So let's talk about what that actually means Here I have some great examples used it is too much code to go over in a short talk so I Would recommend looking at them. These are great examples for simple machine learning. So this is using naive buys or Simple linear approaches linear SVM and what it's doing is it's going to take the vector and it's going to chart it to Whatever it is whatever you're using as labels And if you want to do it on a document approach You're essentially taking a vector of the document model or the sentence model it depends and they have different results and Then you're passing it as a labeled sentence a labeled sentence is actually a model in Jensom So if you're using Jensom, you can label your sentences and then pass them directly in let's say Logistic regression or whatever it is you'd like to use. This is like a simple approach, right? These are the shallow machine learnings Another thing to be aware of when you're making these decisions is you're choosing your n-grams, right? So you have to choose unigram or bi-gram or how do you want to represent these and when you're choosing word embeddings? You're generally making those decisions as you're choosing the word embedding and what they found This is the research by Stanford which enabled them to come up with the matrix vectorization recurrent neural tensor network and what they found is The longer you let your n-grams the more mixed sentiment you have which is kind of logical When you think about it, and I'm going to go on and on and on and have a big long sentence I'm probably going to express maybe differing viewpoints or a mixed emotion and so if you're choosing Unigrams or bi-grams you can see you're way over here and There's some words that are obviously negative and positive But there's probably a lot of words that are just filler words and the further you go towards exploring the n-grams The more complex sentiment you get and the more people are able to say okay Yeah, this phrase is definitely negative and the whole phrase is negative So this is based on the Stanford sentiment word word bank, which is available in Java another approach that people are using for this that's going pretty well is LSTM, which is long short term memory networks And these are deep learning networks and what they're using is they're generally using these word embeddings or these document embeddings And they're pushing them into these deep learning models and the nice thing about LSTM And why it's been doing some really amazing stuff for also Other natural language processing is that it can forget things So it can learn things and it can forget things So because it has that ability what it can do is it can change as it sees more or less of a Representative model so language we know changes and trends come and go and because of that this approach has been really Powerful and you can use it with both the ono and tensor floor. I have some examples There's also the ability to use convolutional neural networks And so initially the idea is that convolutional neural networks are really great at images because you can chunk and label Images and then move accordingly through the pixels right you can crop them But now people are using it for sentences or for documents And they're able to use the word embeddings for each of those and chunk and move through them And then through the same principles of pooling and softmax you can then reach a conclusion and say okay According to my sigmoid softmax. This is positive or negative or neutral And again, I will be posting these so you can take a look at the links It's a little bit different when you're during dealing with deep learning labeled text So what a lot of times you'll have is you'll have the document and then you'll be passing an array And a lot of times people are just using simple arrays like okay This is good is positive. So it's going to point up and this negative is going to point down There's different things happening in this so I would keep an eye on this space in terms of how it how it performs because again Right here. We're having this everything is either positive or negative I'd be curious to see and I know that some of the academics are working on more complex Representations of the labeling and because of that that this might become an interesting space sooner rather than later And then here's where a lot of people are kind of using this dual system Maybe I have a symbol classifier and I have a lexicon and I have auto-tagging and I'm feeding them both into a deep learning system So maybe I have a constant Twitter feed or this or that and I'm feeding it into a symbol classifier And I'm allowing my deep learning system to learn from that classifier This is where a lot of people are starting to experience some big jumps without having to do a lot of work So if you're like me and you're lazy, this might be the best approach There's still obviously some open research But it's an interesting theory this idea that I can just use my simple state of the art classifier and feed it into my deep learning system And my deep learning system will eventually become more intelligent and have a much wider lexicon over time So depending on your system, you might need to handle regularization Pre-processing parsing. This is like the classic NLP problems, right? Is Pre-processing essential? It really depends who you ask. There have been some really interesting research on Sentiment analysis and saying doing too much pre-pre, sorry, doing too much pre-processing Actually is a problem and what ends up happening is you get rid of some of this way that we speak in slang to one another Or the way that we use colloquialisms and by getting rid of those you actually are removing part of how we're expressing our selves So what I would recommend and what generally the literature agrees upon is do minimal pre-processing like maybe just some simple limitization or some simple tagging and then Try it and then do more pre-processing and then try that and see where you hit a nice accuracy in your model And this is kind of where sense to Beck and spacey are really making some cool inroads spacey is a startup based in Berlin and They are doing essentially Parsi McParser face in Python And what they're able to do is they they trained on or they have the English language and the German language Corpus available trained on reddit data and what they're able to do is they're tagging each of the vectors So sense of that is you have this tagged vector So when I talk about Google the organization and Google the verb I mean different things and I might feel differently about them in terms of sentiment So they're making some great inroads when I pass in this is using spacey when I pass in the vector for the winky face Winky face old-school emoji Then I get back some of these other things and I can see that I'm getting back interjections Which is probably a lot more of how we use the winky emoji in the way we express sentiment Like I might say something that I that I don't mean like I hate to computer and then use winky face to say I'm just kidding. So here we can see that he heaps It's spacey is picking up on the fact that I may actually be negating what I'm saying and I'd be really curious to see how spacey gets Incorporated into a lot of these deep learning models. It hasn't been incorporated in most of them yet Finally we get to kind of like what IBM Watson is doing and we're talking about emotions here So there's this famous Diagram that you see almost at every sentiment analysis talk and it's this idea that we have all of these emotions but our emotions can be Made down into just these eight core emotions and then everything else is just some combination of those eight core emotions. I Am no Psychologist so I can't comment on this, but you know it makes somewhat sense So if you're looking into sentiment analysis and you want to start moving away from just positive Native and you want to move into emotions? Let's say you're in charge of like customer service channels and you're trying to say I really want to know when the rage and loathing are activated Because we need to work on that first, but I don't care so much about the positive emotions. I care about more of these angers and IBM Watson has the tone analyzer that's available on the blue mix API and it tries to classify into anger joy fear distrust and sadness It also has the social tendencies and the language style So one of the interesting things that I was asking some researchers when I was able to chat with them is hey Could you maybe say hey? I'm angry, but I'm saying something positive that means maybe I'm being sarcastic or ironic and What what the response I got is that nobody's really doing that yet, but that would be interesting to hear about So I will hopefully be building something that does something similar to this and taking a look at can we start to? Talk about when our words say one thing But our tone or maybe our tone over time says another and by building these models by even Necessarily saying like you as one person have a lexicon and you speak a certain way Can we actually start to understand sentiment at a much deeper level? We'll get into that as we talk about The next portion which is what is still unsolved which is sadly quite a lot So apologies if you live at the UK and are offended, but I found this to be very funny But yeah humor humor is lost on sentiment analysis models We don't know how to tell when somebody's being funny And we a lot of times can't even tell what they're being funny about So here I have this mixture of this you know social media Presence which is this mixture of images and text and how can we start to you know? We don't have to understand images now and we know how to understand text But there's very few that try to do both and in sentiment This is a really key part because how we're talking online is with this combination so negation is still a really big problem and Here's a unigram and by Graham study that says when you negate a unigram or a by Graham The red line is the assumption that you simply create a negative value for that unigram or by Graham And what we see here is the blue dots have the actual representation. So when I say Yeah, it was not that bad Then I don't actually mean that it was good, right? And so we can start to look at these and talk about hey It's not so simple to say just because even somebody negates an emotion It is some opposite and there's been some really interesting studies about Modifiers and how maybe they can point towards a mathematical representation of negation of a sentiment so back to Stanford sentiment trees, which are these MVR NTN networks and They're actually have gotten pretty advanced at determining negation and overall sentiment of a complex sentence And what they are doing here is they're creating smaller n-grams and they're creating sentiment vectorization for that and then they have a series of logic rules and the logic rules state, okay well, this is more negative than this is positive so overall it is negative and They're doing some really really interesting research again. They have their Java jar that you can download and play around with it's still mainly trained on these movie reviews Which we'll get to in a second. So we still have this problem that it's trained on movie reviews So there's still quite a lot of things that are hard We have mixed sentiment. We have complex motions sarcasm irony humor lost, right? We also have speaker intention and personality and there's been some interesting research in that in terms of trying to determine Who you are as a speaker and if I study you over time? Can I determine who you are as a speaker and then can I get a better idea of what you mean? We also have slang and like new and older phrases So part of William Hamilton study with social scent was looking at sentiment over time And he was pointing out that if you look at historical documents You can't use any normal sentiment analysis because in 1920 people were talking a lot differently about things And we have these new phrases too where you know cool AF means something very different than just cool, right? And so then we also have just general NLP issues So I feel like I would be left out of your Python if I didn't have a Pokemon reference in my slides So I put one in here Which I really hope people in America do anyways because I'm very fearful watching from Berlin about what may or may not happen in November And so yeah, let's put some Pokemon lures and and we shouldn't be pulling places But here's an idea. This is obviously exploring a sentiment but No sentiment analysis would understand this so when we're talking about cultural references We also need an ability to say hey right now Pokemon means emphasis We also have speaking with images and Gifts right so now that Giffy is part of Twitter This is important, right? I would not Understand the sentiment of this tweet if I couldn't also look at the image and then determine sentiment of the image Right and the really interesting thing is a lot of these are tagged, right? So these are in a tagged database somewhere and maybe somewhere. There's like hashtag hate or hashtag Or whatever it is and I can apply sentiment to these and then I can pull them out Pull pull out the visual representation and put it back into words and use those words to make a decision Another really big problem with these is speed speed and memory So if you ever try to make your own word embeddings like I hope that you have a Server somewhere set up with a lot of memory and you have a lot of time on there because sometimes compiling these word embeddings Glove is actually a little bit faster a lot faster almost half the time But word to back if you're making your own word embeddings it can take 1415 hours and if you're using glove it can end up eating up gigabytes and gigabytes of RAM So if you're making these embeddings yourself it can be become a real problem and a performance bottleneck both for how do I keep? Feeding my model and also how do I you know apply it? Let's say in a real-time situation so what some folks in Munchen have been studying is the ability to create dense word vectors and dense document vectors and they created this idea of Densify and what they were able to see I don't know if you can see over in the corner But they were able to get it from English words for executions per second to the densified model at 178 with only a slight difference in accuracy so the more solutions we find for sentiment analysis the more problems we have and Depending on especially if you're working in different languages this can get even more difficult What some people are doing that is interesting is attempting to do aggregation within lexicon So if I aggregate everybody speaking English in this particular city what they're finding is maybe there's more agreement within that community on sentiment and words So that's something to look at I also haven't seen a lot of studies on ensemble methods And I think that that's coming into vogue as well as character level embeddings So character level embeddings are the idea that we can predict and attune ourselves on a character level given a language So here is a graphic. I made it's very tongue-in-cheek. It's sorry if it's a little small But this is like my own version of Andreas mules scikit-learn flowchart Which basically says if you have movie reviews or any reviews you're set and if you have anything else and you don't have Money and time and samples. You're pretty much screwed. So sorry about that And I'll So have we solved sentiment and else Not really But if you have time or are willing to work on building a lexicon particularly for your model what they've found is that there can be some really great Winds so I found a lot of papers and also a lot of companies that are building their own Corpora and their own lexicon and they're achieving in the 90th percentile So I want to say thanks you can reach out if you have any questions I'll be posting the slides so you can read all the papers and Look at the code that I referenced for the LSTM as well as the CNN and the simple Simple model architecture All right everyone we have a time for two or three questions. So who would like a first one? Do-do-do-do if you want to leave desperately and go and get coffee, please do so very quietly and respectfully Okay, here we go Hi, thanks for the talk. It's great. I wonder if you had a chance to look at Google sentiment analysis API. They just released this week Look at that. I do have a bookmarked, but I'll let you know when I do the problem really is is that a lot of these really depend on the lexicon and Google is likely using their word embeddings and their word embeddings have problems because everybody else is also using their word embeddings for sentiment So I would doubt it's massively better than what's available from Stanford Which is generally in the high 70s percent low 80s But I'd be curious to see and and if you have done any have you used it yourself? Yeah It was a little there's so much happening. It was hard to keep up with everything and also prepare the talk Thanks very much for the talk. I'm a linguist by training So a lot of the examples you mentioned Really rely like the being able to accurately tell but the sentiment is really relies on knowing a lot about the context So how how much do you think we can actually expect? How accurate do you think we expect to be just based on the text because it seems like people are really really pouring a lot of effort and time into Making the text model is really great and this is wonderful But maybe there's just a cap at how well we can do only based on text I agree to a certain degree to that statement and basically what I'm curious about is more when we start to look at these dependency models and we're start to able to detect stance and phrases that if I'm using a series of phrases Directed at a particular object and particularly if I can do that in a multilingual situation Which people are starting to prove that you can do that that's gonna take sentiment analysis really to the next level because if I can start to say this cluster of words applies to this object and It's obviously negative or it's obviously positive Then I can start to make a little bit of these leaps and bounds and moving away from just this idea that Somehow words around other words So the better our parsers get and the better the more we incorporate the parsers into our models the better Sentiment analysis has gotten over time This question way up at the top Sorry, thank you amazing talk Catherine. That's that's really interesting It seems to me it's a bit like the previous question there this sentiment analysis is really a problem of hard AI and I wonder whether It's going to be the thing that Helps hard AI or whether hard AI is going to Give you the the ultimate goal in in sentiment analysis. Does that does that make sense? Yeah, it does and I was really really impressed with some of the deeper learning models that have been coming out of this and the Fact that they're actually having really really good accuracy with very little training So there's actually some great papers and I'm happy to reference them and post them There's some great trainers of saying. Ah, yeah Well, we just decided to play around with it for a month And we were able to nearly get to state-of-the-art fine-tuned systems So I really think that deep learning is likely the answer here and as deep learning evolves I think sentiment analysis will too because we've seen what it's done in terms of natural language generation and if it's making these inroads in natural language generation and we're getting these networks that are able to Start to understand what we mean or what we're trying to say then we can start to maybe predict how we feel But the problem is the sentiment analysis field is still like very very much a Closed model most of the places that are doing it are owned by places where they can't open source their code metamined and So I think that there's this pressure on us as like open source developers to try and keep up with these things That are happening behind closed doors And that's all we have time for folks. So I think you'll join me in that thank you in Kjam once more


Experts are divided over the passage of the Chattered Institute of Computer Forensics of Nigeria, another related martyrs bill. Some of the experts who attended a public hearing on the bill spoke in favour of its passage, while others spoke against it. One of the supporters is the president of the Chattered Institute of Computer Forensics of Nigeria, Professor Peter Olayewola, who is advocating a speedy passage. These two bills have scaled first and second reading in the House, but there is a need to get an expert view before giving a full first of the law. Chairman of the Committee Honourable Benila says the bill could not have come at a better time like this when the country is faced with different forms of cybercrime and criminality, as well as youth restiveness due to lack of job. Because of the critical role of computer forensics in crime detection, reliable based evidence, prosecution, and balanced judicial pronouncements, this bill could not have come at a better time than now, that the nation is faced with a myriad of security challenges. The bill seeks to provide electronic evidence in solving all manners of crimes without exception. The speaker who was represented by Deputy Majority Leader Honourable Peter Batterson expresses desire that the bill when passed will address the concerns of citizens. Both of these bills contain important provisions and seek to make long lasting changes. They are also both establishment bills that will impose significant and long-standing financial obligations on the public halls. For that reason alone, if nothing else, they deserve our thorough appraiser. Sponsors of the bill gave synopsis on the bill and while it must be passed now. Speaking in support of the bill, Professor Peter O'Layan-Wole espoused its benefits, saying it will reduce the countries over dependence on foreign technology, whereby encouraging indigenous technology. In real life, we now have situations whereby somebody who's gone to sleep with a woman, after sleeping with the woman, she will, I mean, he will take a video of the woman, of when they were having fun and post it on social media. Now, how does that type of person, the lady, or even because the case can be delivered out? How does that person, that group person, how does they seek to address, how does they seek protection? This is one of the reasons why we need a chartered institute of computer forensics of Nigeria. The bill will be considered for passage at the Committee of Whole, when all the views from the public would have been properly aggregated.


Hi guys and welcome back to this video today I want to talk a little bit about my personal struggle with nursing burnout and kind of talk to you about a really Personal topic in my life that I have not been open yet about sharing with you guys and That is how my mental health has been I think when you guys watch my videos you get this perception of who I am and I hope that it's a positive one And you see that I'm like this happy go lucky bubbly outgoing person and that's for the most part who I am but I have to be honest and share with you guys something that's been weighing on me and That is my struggle with some postpartum anxiety And how it has affected me in my nursing career after giving birth to my daughter back in October I had three months off to enjoy with her and then I went back to work And when I went back to work, I realized that some things had really changed within myself and Now as a nurse I was looking at all of my patients as if they were someone's mom Or maybe someone's son or someone's daughter if they were a younger patient I was would think wow this patient has a mom like their mom is Probably still alive or if they ever may be an older patient I'd be like this is someone's mom someone's mom is like really really sick and I just felt like I had all this pressure on myself to not make any mistakes because if I made a mistake Or did something to harm someone or hurt someone in their life. I could like never forgive myself and it was really not a healthy Way to think and I was bringing that home with me as well And I think a lot of this has to do with just after you give birth Your hormones changed and now you just have this whole new purpose and perception of life I had my daughter. She's my everything and I just I couldn't imagine anything happening to her and To know that someone else has that same love for my patients. It really just Put a lot of pressure on myself and it's not that I didn't enjoy what I was doing It's just that it was causing me a lot of stress and anxiety at work I talk about this because I know a lot of you probably struggle with similar things Maybe not necessarily with thinking everyone's a mom or baby or something like that But maybe you have something else that's affecting you in your personal life That's carrying over into work and it's causing you a lot of anxiety This is a slippery slope to be on because it really contributes to nurse burnout and your happiness at work And you've invested so much time in your nursing career that you really should be happy in what you do so I want to show something pretty big that I've done to help me with this struggle and That is I've started going to therapy for it And I want to be honest that I've started going to therapy for it because I think we should break the stigma behind Mental health and we should support one another and know that people are going through these struggles And we are not perfect human beings. We are not perfect nurses And I have no shame in saying that I'm getting help for this So I started to go through therapy through the app called better help and this video is sponsored by better help And I'm super thankful that they reached out to me because it actually came at a perfect time I was actually just talking to my husband a couple weeks ago about you know I really think I need to go to therapy for this. I need to talk to someone I need to get some better coping mechanisms for how I can think about these situations and handle these situations While I'm at work and in my personal life. And so when they reach out to me, I was like It's a sign. I have to do this So better help is an app that you can use to access professional counselors worldwide Who are certified and able to help you there are four modes of communication You can reach them through the phone a tablet a computer or you can privately message them and they respond back to you When I signed up I got matched with a counselor within 24 hours And she actually happens to live locally in my area which was kind of neat And I've been just messaging back and forth with her about some of the things that I've been thinking and struggling with And getting some help on how to change my mindset when it comes to these things I like the messaging back and forth aspect because it for me It kind of feels weird sometimes when you talk to someone in person But I like that that option is available if I want to schedule a phone call with her All of the therapists on there are licensed by their state board and It's nice because if you don't like the one that you're talking to you can easily find another one Another thing I really like is that it's a monthly subscription to receive help They have financial aid available and you're not paying per session It really takes the stress and pressure out of Making sure that one session is like everything you'd hoped for when you know You can just always message your counselor Or you can always schedule a phone call whether that's once a week or every other week or maybe twice a week And receive the help that you need it's important to know that better help is not a crisis line So if you are experiencing a mental health crisis or emergency I will include a resource for you down below that you can access in the description box I think the biggest takeaway so far that I've had from therapy is that I need to give myself permission to not be perfect and I guess It's like I've always known this but I haven't known this until someone pointed it out I put a lot of pressure on myself to be perfect and to do everything and to do everything well and Correct and right into the best potential Which is a good Quality to have but it also can be very stressful and weigh you down and to have that Sense of always needing to be perfect at everything and especially at work puts a lot of pressure and anxiety on Someone and the only person putting it on me is myself So it's really helpful to just have that permission to Let go you don't have to be perfect and if we make mistakes We're going to learn from them and grow from them and be better nurses because of them I'm excited to keep doing the therapy and to just learn different coping mechanisms with My personal life and with my nursing life And I hope that if you're struggling with something whether it's a relationship or your job or maybe nursing school is really overwhelming for you I'm giving you permission to receive help do it for yourself. You don't have to be ashamed or embarrassed They don't even have to see what you look like or hear your voice You can literally like message them through the app. So thank you better help for sponsoring this video I will have a link down below so you guys can easily go download the app Or check it out on your computer or tablet and I'm happy to talk more about this Or if you guys have any specific questions, please ask me. I'm an open book. I want to share I want to help you guys because I know I'm not the only one struggling with this Let me know in the comments below and thanks for watching Make sure you're subscribed to my channel and I'll see you guys next time