Sentiment analysis

Known as: Opinion mining 
Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to… (More)
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Topic mentions per year

Topic mentions per year

1991-2017
0500100019912017

Papers overview

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Highly Cited
2014
Highly Cited
2014
We present a method that learns word embedding for Twitter sentiment classification in this paper. Most existing algorithms for… (More)
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Highly Cited
2011
Highly Cited
2011
We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses… (More)
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Highly Cited
2011
Highly Cited
2011
In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the… (More)
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Highly Cited
2011
Highly Cited
2011
We examine sentiment analysis on Twitter data. The contributions of this paper are: (1) We introduce POS-specific prior polarity… (More)
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Highly Cited
2010
Highly Cited
2010
  • Bing Liu
  • Handbook of Natural Language Processing
  • 2010
Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective… (More)
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Highly Cited
2010
Highly Cited
2010
Microblogging today has become a very popular communication tool among Internet users. Millions of users share opinions on… (More)
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Highly Cited
2010
Highly Cited
2010
In this work we present SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and… (More)
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Highly Cited
2007
Highly Cited
2007
In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture… (More)
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Highly Cited
2005
Highly Cited
2005
This paper presents a new approach to phrase-level sentiment analysis that first determines whether an expression is neutral or… (More)
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Highly Cited
2003
Highly Cited
2003
This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for… (More)
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