Verbs Speak Loud: Verb Categories in Learning Polarity and Strength of Opinions

Sokolova, Marina; Lapalme, Guy

doi:10.1007/978-3-540-68825-9_30

Cited by 13 publications

(11 citation statements)

References 8 publications

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“…Much of the early research in sentiment focused on adjectives or adjective phrases as the primary source of subjective content in a document (Hatzivassiloglou and McKeown 1997;Hu and Liu 2004;Taboada, Anthony, and Voll 2006), albeit with some exceptions, especially more recently, which have also included the use of adverbs (Benamara et al 2007); adjectives and verbs (Kim and Hovy 2004); adjective phrases (Whitelaw, Garg, and Argamon 2005); two-word phrases (Turney 2002;Turney and Littman 2003); adjectives, verbs, and adverbs (Subrahmanian and Reforgiato 2008); the exclusive use of verbs (Sokolova and Lapalme 2008); the use of non-affective adjectives and adverbs Lapalme 2009a, 2009b); or rationales, words and phrases selected by human annotators (Zaidan and Eisner 2008). In general, the SO of an entire document is the combined effect of the adjectives or relevant words found within, based upon a dictionary of word rankings (scores).…”

Section: Adjectivesmentioning

confidence: 99%

“…Support Vector Machine (SVM) classifiers have been shown to outperform lexicon-based models within a single domain (Kennedy and Inkpen 2006); they have trouble with cross-domain tasks (Aue and Gamon 2005), however, and some researchers have argued for hybrid classifiers (Andreevskaia and Bergler 2008). Although some of the machine-learningbased work makes use of linguistic features for training (Riloff and Wiebe 2003;Mullen and Collier 2004;Wiebe et al 2004;Kennedy and Inkpen 2006;Ng, Dasgupta, and Niaz Arifin 2006;Sokolova and Lapalme 2008), it nonetheless still suffers from lack of cross-domain portability. The results presented here suggest that a lexicon-based system could outperform pure or hybrid machine-learning methods in cross-domain situations, though further research would be necessary to establish this point conclusively.…”

Section: Other Related Workmentioning

confidence: 99%

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Lexicon-Based Methods for Sentiment Analysis

Taboada

Brooke

Tofiloski

et al. 2011

Computational Linguistics

2,524

1,463

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We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.

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Section: Adjectivesmentioning

confidence: 99%

Section: Other Related Workmentioning

confidence: 99%

Lexicon-Based Methods for Sentiment Analysis

Taboada

Brooke

Tofiloski

et al. 2011

Computational Linguistics

2,524

1,463

View full text Add to dashboard Cite

show abstract

“…This point is also backed up by Abercrombie & Batista-Navarro (2018a), who observe that, the most indicitive features, even of negative polarity, are words not typically thought of as conveying negativity. Where negative adjectives and verbs are present, Sokolova & Lapalme (2008) find that these are highly discriminative features.…”

Section: Performance and Outcomesmentioning

confidence: 88%

Sentiment and position-taking analysis of parliamentary debates: a systematic literature review

Abercrombie

Batista-Navarro

2020

J Comput Soc Sc

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Parliamentary and legislative debate transcripts provide access to information concerning the opinions, positions and policy preferences of elected politicians. They attract attention from researchers from a wide variety of backgrounds, from political and social sciences to computer science. As a result, the problem of automatic sentiment and position-taking analysis has been tackled from different perspectives, using varying approaches and methods, and with relatively little collaboration or cross-pollination of ideas. The existing research is scattered across publications from various fields and venues. In this article we present the results of a systematic literature review of 61 studies, all of which address the automatic analysis of the sentiment and opinions expressed and positions taken by speakers in parliamentary (and other legislative) debates. In this review, we discuss the available research with regard to the aims and objectives of the researchers who work on these problems, the automatic analysis tasks they undertake, and the approaches and methods they use.We conclude by summarizing their findings, discussing the challenges of applying computational analysis to parliamentary debates, and suggesting possible avenues for further research.

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“…Interesting is the exclusive use of verbs by Sokolova and Lapalme (2008), perhaps because, in some contexts, direct evaluation as expressed by positive and negative adjectives is avoided. (The authors studied consumer reviews and US Congressional debates.)…”

Section: Which Words and Phrasesmentioning

confidence: 99%

Sentiment Analysis: An Overview from Linguistics

Taboada

2016

Annu. Rev. Linguist.

185

109

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Sentiment analysis is a growing field at the intersection of linguistics and computer science that attempts to automatically determine the sentiment contained in text. Sentiment can be characterized as positive or negative evaluation expressed through language. Common applications of sentiment analysis include the automatic determination of whether a review posted online (of a movie, a book, or a consumer product) is positive or negative toward the item being reviewed. Sentiment analysis is now a common tool in the repertoire of social media analysis carried out by companies, marketers, and political analysts. Research on sentiment analysis extracts information from positive and negative words in text, from the context of those words, and from the linguistic structure of the text. This brief review examines in particular the contributions that linguistic knowledge can make to the task of automatically determining sentiment.

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Verbs Speak Loud: Verb Categories in Learning Polarity and Strength of Opinions

Cited by 13 publications

References 8 publications

Lexicon-Based Methods for Sentiment Analysis

Lexicon-Based Methods for Sentiment Analysis

Sentiment and position-taking analysis of parliamentary debates: a systematic literature review

Sentiment Analysis: An Overview from Linguistics

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