jurafsky&martin_3rdEd_17 (1).pdf

In the next sections we introduce lexicons for

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. In the next sections we introduce lexicons for sentiment, semi-supervised algorithms for inducing them, and simple algorithms for using lexicons to perform sentiment analysis. We then turn to the extraction of other kinds of affective meaning, beginning with emotion, and the use of on-line tools for crowdsourcing emotion lexicons, and then proceeding to other kinds of affective meaning like interpersonal stance and personality. 18.1 Available Sentiment Lexicons The most basic lexicons label words along one dimension of semantic variability, called ”sentiment”, ”valence”, or ”semantic orientation”. In the simplest lexicons this dimension is represented in a binary fashion, with a wordlist for positive words and a wordlist for negative words. The oldest is the General Inquirer (Stone et al., 1966) , which drew on early work in the cognition General Inquirer psychology of word meaning (Osgood et al., 1957) and on work in content analysis.
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328 C HAPTER 18 L EXICONS FOR S ENTIMENT AND A FFECT E XTRACTION The General Inquirer is a freely available web resource with lexicons of 1915 posi- tive words and 2291 negative words (and also includes other lexicons we’ll discuss in the next section). The MPQA Subjectivity lexicon (Wilson et al., 2005) has 2718 positive and 4912 negative words drawn from a combination of sources, including the General Inquirer lists, the output of the Hatzivassiloglou and McKeown (1997) system de- scribed below, and a bootstrapped list of subjective words and phrases (Riloff and Wiebe, 2003) that was then hand-labeled for sentiment. Each phrase in the lexicon is also labeled for reliability (strongly subjective or weakly subjective). The polar- ity lexicon of (Hu and Liu, 2004b) gives 2006 positive and 4783 negative words, drawn from product reviews, labeled using a bootstrapping method from WordNet described in the next section. Positive admire, amazing, assure, celebration, charm, eager, enthusiastic, excel- lent, fancy, fantastic, frolic, graceful, happy, joy, luck, majesty, mercy, nice, patience, perfect, proud, rejoice, relief, respect, satisfactorily, sen- sational, super, terrific, thank, vivid, wise, wonderful, zest Negative abominable, anger, anxious, bad, catastrophe, cheap, complaint, conde- scending, deceit, defective, disappointment, embarrass, fake, fear, filthy, fool, guilt, hate, idiot, inflict, lazy, miserable, mourn, nervous, objection, pest, plot, reject, scream, silly, terrible, unfriendly, vile, wicked Figure 18.2 Some samples of words with consistent sentiment across three sentiment lexi- cons: the General Inquirer (Stone et al., 1966) , the MPQA Subjectivity lexicon (Wilson et al., 2005) , and the polarity lexicon of Hu and Liu (2004b) . 18.2 Semi-supervised induction of sentiment lexicons Some affective lexicons are built by having humans assign ratings to words; this was the technique for building the General Inquirer starting in the 1960s (Stone et al., 1966) , and for modern lexicons based on crowd-sourcing to be described in Section 18.5.1 . But one of the most powerful ways to learn lexicons is to use semi- supervised learning.
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