Sentiment analysis

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Sentiment Analysis tool



Types of sentiment analysis[edit]


Early work in that area includes Turney[1] and Pang[2] who applied different methods for detecting the polarity of product reviews and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang[3] and Snyder[4] among others: Bo and Lilian[3] expanded the basic task of classifying a movie review as either positive or negative to predicting star ratings on either a 3 or a 4 star scale, while Snyder[4] performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale). Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified. Moreover it can be proven that specific classifiers such as the Max Entropy[5] and the SVMs[6] can benefit from the introduction of neutral class and improve the overall accuracy of the classification.

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral or positive sentiment with them are given an associated number on a -10 to +10 scale (most negative up to most positive) and when a piece of unstructured text is analyzed using natural language processing, the subsequent concepts are analyzed for an understanding of these words and how they relate to the concept.[citation needed] Each concept is then given a score based on the way sentiment words relate to the concept, and their associated score. This allows movement to a more sophisticated understanding of sentiment based on an 11 point scale. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.[7]

Subjectivity/objectivity identification[edit]

This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective.[8] This problem can sometimes be more difficult than polarity classification.[9] The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su,[10] results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang[11] showed that removing objective sentences from a document before classifying its polarity helped improve performance.

Feature/aspect-based sentiment analysis[edit]

It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank.[12] A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food.[13] This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral.[14] The automatic identification of features can be performed with syntactic methods or with topic modeling.[15][16] More detailed discussions about this level of sentiment analysis can be found in Liu's work.[17]

Methods and features[edit]

Existing approaches to sentiment analysis can be grouped into four main categories: keyword spotting, lexical affinity, statistical methods, and concept-level techniques.[18] Keyword spotting classifies text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.[19] Lexical affinity not only detects obvious affect words, it also assigns arbitrary words a probable “affinity” to particular emotions.[20] Statistical methods leverage on elements from machine learning such as latent semantic analysis, support vector machines, "bag of words" and Semantic Orientation — Pointwise Mutual Information (See Peter Turney's[1] work in this area). More sophisticated methods try to detect the holder of a sentiment (i.e. the person who maintains that affective state) and the target (i.e. the entity about which the affect is felt).[21] To mine the opinion in context and get the feature which has been opinionated, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.[22] Unlike purely syntactical techniques, concept-level approaches leverage on elements from knowledge representation such as ontologies and semantic networks and, hence, are also able to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.[23]

Open source software tools deploy machine learning, statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media.[22] Knowledge-based systems, instead, make use of publicly available resources, e.g., WordNet-Affect,[24] SentiWordNet,[25] and SenticNet,[26][27] to extract the semantic and affective information associated with natural language concepts. Sentiment Analysis can also be performed on visual content i.e. images and videos. One of the first approach in this direction is SentiBank[28] utilizing an adjective noun pair representation of visual content.


Sometimes, the structure of sentiments and topics is fairly complex. Also, the problem of sentiment analysis is non-monotonic in respect to sentence extension and stop-word substitution (compare THEY would not let my dog stay in this hotel vs I would not let my dog stay in this hotel). To address this issue a number of rule-based and reasoning-based approaches have been applied to sentiment analysis, including Defeasible Logic Programming.[30] Also, there is a number of tree traversal rules applied to syntactic parse tree to extract the topicality of sentiment in open domain setting.[31][32]


The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by precision and recall. However, according to research human raters typically agree 79%[33] of the time (see Inter-rater reliability).

Thus, a 70% accurate program is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about any answer .[34] More sophisticated measures can be applied, but evaluation of sentiment analysis systems remains a complex matter. For sentiment analysis tasks returning a scale rather than a binary judgement, correlation is a better measure than precision because it takes into account how close the predicted value is to the target value.

Sentiment analysis and Web 2.0[edit]


One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in e-communities through sentiment analysis.[38] The CyberEmotions project, for instance, recently identified the role of negative emotions in driving social networks discussions.[39]


Even though short text strings might be a problem, sentiment analysis within microblogging has shown that Twitter can be seen as a valid online indicator of political sentiment. Tweets’ political sentiment demonstrates close correspondence to parties’ and politicians’ political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.[40]

Resources for sentiment analysis[edit]

Sentiment vocabularies and annotated word lists:

Online sentiment analyzers:

  • AlchemyAPI[45] (commercial)
  • BitextAPI[46] (commercial)
  • Semantria[47] (commercial)
  • Sentiment140[48] (commercial, for Twitter)
  • Stanford NLP[49] (academic)
  • Werfamous[50] (free)
  • WordStat[51] (commercial)
  • Buzzlogix[52] (free and commercial versions)

Annotated corpora (documents with manual annotations of sentiment that can be used to evaluate algorithms):

  • Twitter dataset in 4 languages[53] (12,500 tweets)

See also[edit]


  13. ^ Cataldi, Mario; Ballatore, Andrea; Tiddi, Ilaria; Aufaure, Marie-Aude (2013-06-22). "Good location, terrible food: detecting feature sentiment in user-generated reviews". Social Network Analysis and Mining 3 (4): 1149–1163. doi:10.1007/s13278-013-0119-7. ISSN 1869-5450. 
  16. ^ Titov, Ivan; McDonald, Ryan (2008-01-01). "Modeling Online Reviews with Multi-grain Topic Models". Proceedings of the 17th International Conference on World Wide Web. WWW '08 (New York, NY, USA: ACM): 111–120. doi:10.1145/1367497.1367513. ISBN 978-1-60558-085-2. 
  18. ^ Cambria, Erik; Schuller, Björn; Xia, Yunqing; Havasi, Catherine (2013). "New Avenues in Opinion Mining and Sentiment Analysis". IEEE Intelligent Systems 28 (2): 15–21. doi:10.1109/MIS.2013.30. 
  20. ^ Stevenson, Ryan; Mikels, Joseph; James, Thomas (2007). "Characterization of the Affective Norms for English Words by Discrete Emotional Categories" (PDF). Behavior Research Methods 39 (4): 1020–1024. 
  22. ^ a b Dey, Lipika; Haque, S. K. Mirajul (2008). "Opinion Mining from Noisy Text Data". Proceedings of the second workshop on Analytics for noisy unstructured text data, p.83-90.  Cite error: Invalid <ref> tag; name "Dey.2BHaque08" defined multiple times with different content (see the help page).
  23. ^ Cambria, Erik; Hussain, Amir (2012). Sentic Computing: Techniques, Tools, and Applications (PDF). Springer. 
  24. ^ a b Strapparava, Carlo; Valitutti, Alessandro (2004). "WordNet-Affect: An affective extension of WordNet" (PDF). Proceedings of LREC. pp. 1083–1086.  Cite error: Invalid <ref> tag; name "Strapparava04" defined multiple times with different content (see the help page).
  25. ^ a b Baccianella, Stefano; Esuli, Andrea; Sebastiani, Fabrizio (2010). "Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining" (PDF). Proceedings of LREC. pp. 2200–2204. Retrieved 2014-04-05.  Cite error: Invalid <ref> tag; name "Esuli06" defined multiple times with different content (see the help page).
  26. ^ a b "SenticNet". 
  27. ^ a b Cambria, Erik; Olsher, Daniel; Rajagopal, Dheeraj (2014). "SenticNet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis" (PDF). Proceedings of AAAI. pp. 1515–1521.  Cite error: Invalid <ref> tag; name "Cambria10" defined multiple times with different content (see the help page).
  29. ^ "Case Study: Advanced Sentiment Analysis". Retrieved 18 October 2013. 
  34. ^ Roebuck, K. Sentiment Analysis: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors. 
  36. ^ "Sentiment Analysis on Reddit". Retrieved 10 October 2014. 
  39. ^ Condliffe, Jamie. "Flaming drives online social networks ", NewScientist, 2010-12-07. Retrieved on 2010-12-13.
  41. ^ "generating the Affective Norms for English Words (ANEW) dataset". 
  42. ^ Stevenson, Ryan A.; Mikels, Joseph A.; James, Thomas W. (2007-11-01). "Characterization of the Affective Norms for English Words by discrete emotional categories". Behavior Research Methods 39 (4): 1020–1024. doi:10.3758/BF03192999. ISSN 1554-351X. 
  43. ^ "SentiWordNet". 
  44. ^ Manuela Speranza, FBK. "WordNet Domains". 
  45. ^
  46. ^
  47. ^ "Semantria Web Demo -". 
  48. ^ "API - Sentiment140 - A Twitter Sentiment Analysis Tool". 
  49. ^ "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank". Deeply Moving: Deep Learning for Sentiment Analysis. 
  50. ^ "A Twitter and web sentiment analysis tool". 
  51. ^ "Content analysis software for sentiment analysis". 
  52. ^ "Text Analysis & Sentiment Analysis API | Buzzlogix". Buzzlogix | Text Analysis API (in en-US). Retrieved 2015-11-23. 
  53. ^ "DAI-Labor > Competence Centers > CC IRML > Datasets > Annotated Sentiment Dataset". 

Further reading[edit]