Sentiment Analysis: An Overview from Linguistics

Sentiment analysis is a growing field at the intersection of linguistics and computer science, which attempts to automatically determine the sentiment, or positive/negative opinion, 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 towards 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 the linguistic structure of the text. This brief survey examines in particular the contributions that linguistic knowledge can make to the problem of automatically determining sentiment

Discourse Relations and Evaluation

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

Extraction of opinionated profiles from comments on web news

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

We usually don't like going to the dentist : using common sense to detect irony on Twitter

A

Sentiment Sentence Extraction Using a Hierarchical Directed Acyclic graph Structure and Bootstrap Approach

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200