The contribution of nonveridical rhetorical relations to evaluation in discourse

Trnavac, Radoslava; Taboada, Maïté

doi:10.1016/j.langsci.2011.10.005

Cited by 28 publications

(27 citation statements)

References 27 publications

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“…The authors also highlight the importance of irrealis mood on polarity classification tasks, ignoring the sentiment reflected by these sentences. However, some types of irrealis such as conditional relations can contribute to evaluation in discourse, as pointed out in Trnavac and Taboada ().…”

Section: Background and Related Workmentioning

confidence: 99%

On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages

Vilares

Alonso

Gómez-Rodríguez

2015

Asso for Info Science & Tech

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Millions of micro texts are published every day on Twitter. Identifying the sentiment present in them can be helpful for measuring the frame of mind of the public, their satisfaction with respect to a product or their support of a social event. In this context, polarity classification is a subfield of sentiment analysis focussed on determining whether the content of a text is objective or subjective, and in the latter case, if it conveys a positive or a negative opinion. Most polarity detection techniques tend to take into account individual terms in the text and even some degree of linguistic knowledge, but they do not usually consider syntactic relations between words. This article explores how relating lexical, syntactic and psychometric information can be helpful to perform polarity classification on Spanish tweets. We provide an evaluation for both shallow and deep linguistic perspectives. Empirical results show an improved performance of syntactic approaches over pure lexical models when using large training sets to create a classifier, but this tendency is reversed when small training collections are used.

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Section: Background and Related Workmentioning

confidence: 99%

On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages

Vilares

Alonso

Gómez-Rodríguez

2015

Asso for Info Science & Tech

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“…In our previous work on evaluation and sentiment analysis in text (Taboada & Grieve, 2004;Voll & Taboada, 2007;Taboada et al, 2009;Taboada et al, 2011;Trnavac & Taboada, 2012, 2014, we have observed that certain discourse relations affect the interpretation of the evaluation contained therein. Consider the following examples (1) -(3) from the Simon Fraser Review Corpus (Taboada, 2008) in which the semantics of embedded evaluators within concessive, conditional and elaborative sentences is affected by the corresponding discourse relation:…”

Section: Introductionmentioning

confidence: 84%

“…The basic premise is that words have a prior polarity, i.e., their polarity in isolation, in a sort of dictionary sense, but also contextual polarity, affected by the context in which they appear (Wilson et al, 2009). Trnavac and Taboada (2012) carry out a corpus analysis on movie and book reviews to examine how nonveridical markers (i.e., negation, modals, imperatives, questions, habituals, intensional verbs, subjunctives) and discourse relations (concessive and conditional) contribute to the expression of evaluation in discourse. The authors conclude that nonveridical elements in the majority of cases modify polarity at the local level (level of the clause), while discourse relations derive the changes from the combination of two or more clauses.…”

Section: Introductionmentioning

confidence: 99%

Discourse relations and evaluation

Trnavac¹,

Das²,

Taboada³

2016

Corpora

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We examine the role of discourse relations (relations between propositions) in the interpretation of evaluative or opinion words. Through a combination of Rhetorical Structure Theory or RST (Mann & Thompson, 1988) and Appraisal Theory (Martin & White, 2005), we analyze how different discourse relations modify the evaluative content of opinion words, and what impact the nucleus-satellite structure in RST has on the evaluation. We conduct a corpus study, examining and annotating over 3,000 evaluative words in 50 movie reviews in the SFU Review Corpus (Taboada, 2008) with respect to five parameters: word category (nouns, verbs, adjectives or adverbs), prior polarity (positive, negative or neutral), RST structure (both nucleus-satellite status and relation type) and change of polarity as a result of being part of a discourse relation (Intensify, Downtone, Reversal or No Change). Results show that relations such as Concession, Elaboration, Evaluation, Evidence and Restatement most frequently intensify the polarity of the opinion words, although the majority of evaluative words (about 70%) do not undergo changes in their polarity because of the relations they are a part of. We also find that most opinion words (about 70%) are positioned in the nucleus, confirming a hypothesis in the literature, that nuclei are the most important units when extracting evaluation automatically.

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“…Nonveridicality is wider, including all contexts which are not veridical, i.e., which are not based on truth or existence (Giannakidou 1995, Zwarts 1995. In previous work, we have defined the class of nonveridical operators as including negation (see next section), modal verbs, intensional verbs (believe, think, want, suggest), imperatives, questions, protasis of conditionals, habituals and the subjunctive, in languages which have an expression of subjunctive (Trnavac & Taboada 2012). Consider the effect of the intentional verb thought and the modal would in (1), and the modal plus question in (2), which completely discounts any positive evaluation that may be present in suitable, or more suitable.…”

Section: Intensification and Downtoning; Irrealis And Nonveridicalitymentioning

confidence: 99%

“…A related line of research has been investigating exactly how polarity words change in the context of a discourse relation , Trnavac & Taboada 2012.…”

Section: Discourse Patternsmentioning

confidence: 99%

Sentiment Analysis: An Overview from Linguistics

Taboada

2016

Annu. Rev. Linguist.

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156

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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The contribution of nonveridical rhetorical relations to evaluation in discourse

Cited by 28 publications

References 27 publications

On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages

On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages

Discourse relations and evaluation

Sentiment Analysis: An Overview from Linguistics

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