A Survey on Sentiment Analysis and Opinion Mining Techniques
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paper_title: Pulse: Mining Customer Opinions from Free Text

paper_content:

We present a prototype system, code-named Pulse, for mining topics and sentiment orientation jointly from free text customer feedback. We describe the application of the prototype system to a database of car reviews. Pulse enables the exploration of large quantities of customer free text. The user can examine customer opinion “at a glance” or explore the data at a finer level of detail. We describe a simple but effective technique for clustering sentences, the application of a bootstrapping approach to sentiment classification, and a novel user-interface.
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paper_title: Thumbs Up? Sentiment Classification Using Machine Learning Techniques

paper_content:

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we flnd that standard machine learning techniques deflnitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classiflcation, and support vector machines) do not perform as well on sentiment classiflcation as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classiflcation problem more challenging.
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paper_title: Using Emoticons To Reduce Dependency In Machine Learning Techniques For Sentiment Classification

paper_content:

Sentiment Classification seeks to identify a piece of text according to its author's general feeling toward their subject, be it positive or negative. Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic. This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time.
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paper_title: Are SentiWordNet scores suited for multi-domain sentiment classification?

paper_content:

Motivated by the numerous applications of analysing opinions in multi-domain scenarios, this paper studies the potential of a still rarely considered approach to the problem of multi-domain sentiment analysis based on Senti-WordNet as lexical resource. SentiWordNet scores are exploited together with additional features to assign a polarity to a text using machine learning. On the other hand, a rule-based approach is studied based on sentiment scores. The introduced methods are tested on single domains of a real-world data set consisting of documents in six different domains, but also in cross-domain settings. The results show that for cross-domain sentiment analysis rule-based approaches with fix opinion lexica are unsuited. For machine-learning based sentiment classification a mixture of documents of different domains achieves good results.
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paper_title: Pulse: Mining Customer Opinions from Free Text

paper_content:

We present a prototype system, code-named Pulse, for mining topics and sentiment orientation jointly from free text customer feedback. We describe the application of the prototype system to a database of car reviews. Pulse enables the exploration of large quantities of customer free text. The user can examine customer opinion “at a glance” or explore the data at a finer level of detail. We describe a simple but effective technique for clustering sentences, the application of a bootstrapping approach to sentiment classification, and a novel user-interface.
---
paper_title: Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews

paper_content:

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g.,"subtle nuances") and a negative semantic orientation when it has bad associations (e.g.,"very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word"excellent"minus the mutual information between the given phrase and the word"poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
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paper_title: Determining The Sentiment Of Opinions

paper_content:

Identifying sentiments (the affective parts of opinions) is a challenging problem. We present a system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion. The system contains a module for determining word sentiment and another for combining sentiments within a sentence. We experiment with various models of classifying and combining sentiment at word and sentence levels, with promising results.
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paper_title: Automatic Identification Of Sentiment Vocabulary: Exploiting Low Association With Known Sentiment Terms

paper_content:

We describe an extension to the technique for the automatic identification and labeling of sentiment terms described in Turney (2002) and Turney and Littman (2002). Their basic assumption is that sentiment terms of similar orientation tend to co-occur at the document level. We add a second assumption, namely that sentiment terms of opposite orientation tend not to co-occur at the sentence level. This additional assumption allows us to identify sentiment-bearing terms very reliably. We then use these newly identified terms in various scenarios for the sentiment classification of sentences. We show that our approach outperforms Turney's original approach. Combining our approach with a Naive Bayes bootstrapping method yields a further small improvement of classifier performance. We finally compare our results to precision and recall figures that can be obtained on the same data set with labeled data.
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paper_title: Learning subjective adjectives from corpora

paper_content:

Subjectivity tagging is distinguishing sentences used to present opinions and evaluations from sentences used to objectively present factual information. There are numerous applications for which subjectivity tagging is relevant, including information extraction and information retrieval. This paper identifies strong clues of subjectivity using the results of a method for clustering words according to distributional similarity (Lin 1998), seeded by a small amount of detailed manual annotation. These features are then further refined with the addition of lexical semantic features of adjectives, specifically polarity and gradability (Hatzivassiloglou & McKeown 1997), which can be automatically learned from corpora. In 10-fold cross validation experiments, features based on both similarity clusters and the lexical semantic features are shown to have higher precision than features based on each alone.
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paper_title: Subjectivity Detection in English and Bengali: A CRF-based Approach

paper_content:

With the proliferation of online reviews and sentiments the Web is becoming more and more useful and important information resource for people. As a result, automatic opinion/sentiment mining has become a hot research topic recently. Extracting opinions from text is a hard semantic problem. Subjectivity Detection is studied as a text classification problem that classifies texts as either subjective or objective. This paper illustrates a Conditional Random Field (CRF) based Subjectivity Detection approach tested on English and Bengali multiple domain corpus to establish its effectiveness over multiple domain perspective. The motivation is to develop generic domain independent solution architecture for a less computerized language like Bengali. A relatively simple and less human interactive technique has been proposed for developing opinion mining resources for Bengali. The features used in the CRF-based classifier could be extracted for any new language with minimum linguistics knowledge. The final classifier has resulted precision values of 76.08% and 79.90% for English and 72.16% and 74.6% for Bengali for the news and blog domains respectively.
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paper_title: Recognizing subjective sentences: a computational investigation of narrative text

paper_content:

Part of understanding third-person fictional narrative text is determining for each sentence whether it takes some character's psychological point of view (is subjective) and, if it does, identifying the character whose point of view is taken (the subjective character). This dissertation is a computational investigation of how a reader can perform these tasks. We identify regularities, found during extensive examinations of naturally occurring texts, in the ways that texts initiate, continue, and resume a character's psychological point of view, and present an algorithm that exploits these regularities in order to recognize subjective sentences and their subjective characters. ::: Linguistic elements (syntactic, lexical, and morphological) are an important source of information for recognizing subjective sentences. While there are some that always indicate that a sentence is subjective, we show that there are many that only potentially do so. In deciding if an element is subjective, we show that it is useful to consider the kinds of things that have appeared since the last subjective sentence (which determine the current text situation). Elements with weak potential to be subjective should usually be interpreted to be subjective only in the most continuous of situations, while those with strong potential to be subjective can be subjective in less continuous situations. ::: The subjective character may be identifiable from the sentence itself. One case we identify is a sentence that reports a character's private state, such as wanting or seeing. We specify a way to distinguish reports from other uses of private-state sentences, so that it can be determined if the subjective character is identifiable from the sentence. ::: We also identify characters who are likely to become subjective characters (called expected subjective characters); if the subjective character is not identifiable from the sentence, it should usually be identified to be one of these. One is the last subjective character and the other is a recent actor; we specify conditions under which these are expected subjective characters and give a heuristic for resolving competition between them. ::: We show that references are understood differently in conversation, subjective sentences, and non-subjective sentences; namely, they are understood with respect to different sets of beliefs. We describe an implementation of the algorithm and demonstrate it in the body of the dissertation.
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paper_title: Mining the peanut gallery: opinion extraction and semantic classification of product reviews

paper_content:

The web contains a wealth of product reviews, but sifting through them is a daunting task. Ideally, an opinion mining tool would process a set of search results for a given item, generating a list of product attributes (quality, features, etc.) and aggregating opinions about each of them (poor, mixed, good). We begin by identifying the unique properties of this problem and develop a method for automatically distinguishing between positive and negative reviews. Our classifier draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation. The best methods work as well as or better than traditional machine learning. When operating on individual sentences collected from web searches, performance is limited due to noise and ambiguity. But in the context of a complete web-based tool and aided by a simple method for grouping sentences into attributes, the results are qualitatively quite useful.
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paper_title: Adding Redundant Features for CRFs-based Sentence Sentiment Classification

paper_content:

In this paper, we present a novel method based on CRF's in response to the two special characteristics of "contextual dependency" and "label redundancy" in sentence sentiment classification. We try to capture the contextual constraints on sentence sentiment using CRFs. Through introducing redundant labels into the original sentimental label set and organizing all labels into a hierarchy, our method can add redundant features into training for capturing the label redundancy. The experimental results prove that our method outperforms the traditional methods like NB, SVM, MaxEnt and standard chain CRFs. In comparison with the cascaded model, our method can effectively alleviate the error propagation among different layers and obtain better performance in each layer.
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paper_title: Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews

paper_content:

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g.,"subtle nuances") and a negative semantic orientation when it has bad associations (e.g.,"very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word"excellent"minus the mutual information between the given phrase and the word"poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
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paper_title: Taking Refuge in Your Personal Sentic Corner

paper_content:

In a world in which web users are continuously blasted by ads and often compelled to deal with user-unfriendly interfaces, we sometimes feel like we want to evade from the sensory overload of standard web pages and take refuge in a safe web corner, in which contents and design are in harmony with our current frame of mind. Sentic Corner is an intelligent user interface that dynamically collects audio, video, images and text related to the user’s current feelings and activities as an interconnected knowledge base, which is browsable through a multi-faceted classification website.
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paper_title: Thumbs Up? Sentiment Classification Using Machine Learning Techniques

paper_content:

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we flnd that standard machine learning techniques deflnitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classiflcation, and support vector machines) do not perform as well on sentiment classiflcation as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classiflcation problem more challenging.
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paper_title: Labeling Emotion in Bengali Blog Corpus – A Fine Grained Tagging at Sentence Level

paper_content:

Emotion, the private state of a human entity, is becoming an important topic in Natural Language Processing (NLP) with increasing use of search engines. The present task aims to manually annotate the sentences in a web based Bengali blog corpus with the emotional components such as emotional expression (word/phrase), intensity, associated holder and topic(s). Ekman’s six emotion classes (anger, disgust, fear, happy, sad and surprise) along with three types of intensities (high, general and low) are considered for the sentence level annotation. Presence of discourse markers, punctuation marks, negations, conjuncts, reduplication, rhetoric knowledge and especially emoticons play the contributory roles in the annotation process. Different types of fixed and relaxed strategies have been employed to measure the agreement of the sentential emotions, intensities, emotional holders and topics respectively. Experimental results for each emotion class at word level on a small set of the whole corpus have been found satisfactory.
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paper_title: Semi-Supervised Polarity Lexicon Induction

paper_content:

We present an extensive study on the problem of detecting polarity of words. We consider the polarity of a word to be either positive or negative. For example, words such as good, beautiful, and wonderful are considered as positive words; whereas words such as bad, ugly, and sad are considered negative words. We treat polarity detection as a semi-supervised label propagation problem in a graph. In the graph, each node represents a word whose polarity is to be determined. Each weighted edge encodes a relation that exists between two words. Each node (word) can have two labels: positive or negative. We study this framework in two different resource availability scenarios using WordNet and OpenOffice thesaurus when WordNet is not available. We report our results on three different languages: English, French, and Hindi. Our results indicate that label propagation improves significantly over the baseline and other semi-supervised learning methods like Mincuts and Randomized Mincuts for this task.
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paper_title: Opinion Mining From Blogs

paper_content:

With the growing popularity of the Web 2.0, we are more and more provided with documents expressing opinions on different topics. Recently, new research approaches were defined in order to automatically extract such opinions on the Internet. Usually they consider that opinions are expressed through adjectives and they extensively use either general dictionaries or experts in order to provide the relevant adjectives. Unfortunately these approach suffer the following drawback: for a specific domain either the adjective does not exist or its meaning could be different from another domain. In this paper, we propose a new approach focusing on two steps. First we automatically extract from the Internet a learning dataset for a specific domain. Second we extract from this learning set, the set of positive and negative adjectives relevant for the domain. Conducted experiments performed on real data show the usefulness of our approach.
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paper_title: Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews

paper_content:

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g.,"subtle nuances") and a negative semantic orientation when it has bad associations (e.g.,"very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word"excellent"minus the mutual information between the given phrase and the word"poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
---
paper_title: Determining The Sentiment Of Opinions

paper_content:

Identifying sentiments (the affective parts of opinions) is a challenging problem. We present a system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion. The system contains a module for determining word sentiment and another for combining sentiments within a sentence. We experiment with various models of classifying and combining sentiment at word and sentence levels, with promising results.
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Title: A Survey on Sentiment Analysis and Opinion Mining Techniques

Section 1: INTRODUCTION
Description 1: Provide an overview of sentiment analysis and opinion mining, including its definition, purpose, and common applications. 

Section 2: PROCESS OF SENTIMENT ANALYSIS FOR TEXT
Description 2: Discuss the general process of sentiment analysis, including key steps such as lexicon generation, subjectivity detection, sentiment polarity detection, sentiment structurization, and summarization.

Section 3: Process of Sentiment Analysis for Text (Lexicon Generation)
Description 3: Explain the creation of sentiment lexicons and discuss various methods and techniques involved, along with historical milestones and related work.

Section 4: Subjectivity Detection
Description 4: Describe how sentiment analysis categorizes text based on its subjective and objective nature, including techniques and related research for detecting subjectivity.

Section 5: Sentiment Polarity Detection
Description 5: Outline the methods used to classify sentiments into different semantic classes, such as positive, negative, and neutral, and cover relevant research in this area.

Section 6: Sentiment Structurization
Description 6: Explain the aspectual sentiment classification techniques that go beyond binary output to provide more detailed sentiment information.

Section 7: Sentiment Summarization-Visualization-Tracking
Description 7: Discuss methods and techniques for summarizing, visualizing, and tracking sentiment data to meet the needs of end users.

Section 8: SENTIMENT ANALYSIS FOR INDIAN LANGUAGES
Description 8: Present the research, techniques, and challenges associated with sentiment analysis for Indian languages, including the creation of SentiWordNet for various languages and related work.

Section 9: Conclusion
Description 9: Summarize the findings and implications of the survey. Discuss the achievements and ongoing challenges in sentiment analysis and opinion mining.

Section 10: Future Work
Description 10: Speculate on potential future research directions, including cross-lingual sentiment sense mapping, developing web service APIs, improving basic resources, and exploring additional Indian languages.