Abstract

Previous research has established that punctuation can be used to communicate nuances of meaning in online writing (McAndrew & De Jonge, 2011). Punctuation, considered a computer mediated communication (CMC) cue, expresses tone and emotion and disambiguates an author's intention (Vandergriff, 2013). Quotation marks as CMC cues can serve pragmatic functions and have been understudied. Some of these functions have been generally described (Predelli, 2003). However, no corpus study has specifically focused on the pragmatic uses of quotations in online text. Consumer reviews, a genre of online text, can directly impact business profits and influence customers' purchasing decisions (Floyd, Freling, Alhoqail, Cho & Freling, 2014). Businesses are investing in sentiment analysis to gauge their target market's opinions (Salehan & Kim, 2016). Sentiment analysis is the computerized appraisal of a text to determine whether its author is expressing a positive or negative opinion (Novak, Smailovic, Sluban & Mozetic, 2015). Sentiment analysis programs are still limited and could be improved in accuracy. Most programs rely on lexicons of words given a pre-determined polarity value (positive or negative) out of context (Novak et al., 2015). However, context is crucial to communication, and sentiment analysis programs could incorporate a better variety of contextual linguistic features to improve their accuracy. Quotations used for pragmatic communication is such a feature. This study discovered seven pragmatic quotation uses in a 2014 Yelp review corpus: Collective Knowledge, Non-standard, Grammatical, Non-literal, Narrative, Idiolect, and Emphasis. An ANOVA and Tukey HSD test were performed, and the results were significant. Pragmatic category accounted for 15% of the variance in review star rating. The Collective Knowledge category and the Narrative and Non-literal categories were significantly different from each other. The Collective Knowledge category showed a correlation with positive sentiment, while the Narrative and Non-literal categories displayed a correlation with negative sentiment. These three categories are likely present in several types of online text, making them valuable for further sentiment analysis research. If these pragmatic patterns could be detected automatically, they could be used in sentiment algorithms to give a more accurate picture of author opinion.

Degree

College and Department

Humanities; Linguistics and English Language

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