Difference between revisions of "Sentiment analysis"

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== Introduction ==
 
== Introduction ==
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Sentiment analysis or opinion mining refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials.
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'''Sentiment analysis''' or '''opinion mining''' refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials, also to identify the polarity of the extracted subjective information.
  
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Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall tonality of a document. The attitude may be his or her judgment or evaluation (see appraisal theory), affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader).
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More information can be found in the survey book [1].
  
 
== Methods ==
 
== Methods ==
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Computers can perform automated sentiment analysis of digital texts, using 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 [2] 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 named entity or target whose affective state one is interested in) [13]. 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 [14].
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Computers can perform automated sentiment analysis of digital texts, using elements from machine learning such as [[UsesMethod::Latent semantic indexing|latent semantic analysis]], [[UsesMethod::Support vector machine classifier learning|support vector machines]], [[UsesMethod::Naive Bayes classifier learning|Naive Bayes]] with "[[UsesMethod::bag of words]]" and Semantic Orientation — [[UsesMethod::Pointwise mutual information|Pointwise Mutual Information]]. 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 named entity or target whose affective state one is interested in). 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.
  
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In Sentic computing [15], a multi-disciplinary approach to opinion mining and sentiment analysis, text processing is not based on statistical learning models but rather on common sense reasoning tools and affective ontologies. Differently from statistical classification, which generally requires large inputs and thus cannot appraise texts with satisfactory granularity, Sentic Computing enables the analysis of documents not only on the page- or paragraph-level but also on the sentence-level.
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Open source software tools deploy machine learning, statistics, and natural language processing techniques allowing to automate the sentiment analysis task on large collections of texts like for example web pages, online news, internet discussion groups, online reviews, web blogs, and social media like for example Twitter.
  
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Open source software tools deploy machine learning, statistics, and natural language processing techniques allowing to automate the sentiment analysis task on large collections of texts like for example web pages, online news, internet discussion groups, online reviews, web blogs, and social media like for example Twitter.
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== Reference ==
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[1] Pang Bo and Lillian Lee. 2008. Opinion mining and sentiment analysis.

Latest revision as of 21:39, 31 March 2011

This is a problem discussed in Social Media Analysis 10-802 in Spring 2011.

Introduction

Sentiment analysis or opinion mining refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials, also to identify the polarity of the extracted subjective information.

More information can be found in the survey book [1].

Methods

Computers can perform automated sentiment analysis of digital texts, using elements from machine learning such as latent semantic analysis, support vector machines, Naive Bayes with "bag of words" and Semantic Orientation — Pointwise Mutual Information. 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 named entity or target whose affective state one is interested in). 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.

Open source software tools deploy machine learning, statistics, and natural language processing techniques allowing to automate the sentiment analysis task on large collections of texts like for example web pages, online news, internet discussion groups, online reviews, web blogs, and social media like for example Twitter.

Reference

[1] Pang Bo and Lillian Lee. 2008. Opinion mining and sentiment analysis.

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