Determining the semantic orientation of terms through gloss classification

By Andrea Esuli, Fabrizio Sebastiani. International Conference on Information and Knowledge Management, 2005

477 citations recorded by the source corpus.

Sentiment classification is a recent subdiscipline of text classification which is concerned not with the topic a document is about, but with the opinion it expresses. It has a rich set of applications, ranging from tracking users' opinions about products or about political candidates as expressed in online forums, to customer relationship management. Functional to the extraction of opinions from text is the determination of the orientation of ``subjective'' terms contained in text, i.e. the determination of whether a term that carries opinionated content has a positive or a negative connotation. In this paper we present a new method for determining the orientation of subjective terms. The method is based on the quantitative analysis of the glosses of such terms, i.e. the definitions that these terms are given in on-line dictionaries, and on the use of the resulting term representations for semi-supervised term classification. The method we present outperforms all known methods when tested on the recognized standard benchmarks for this task.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/eb8b47a9df9b458942662491f0886a8b5b904db9

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In Artificial Intelligence

1. Bing Liu
2. Ting-Wei Liu
3. Bing Qin
4. Duyu Tang
5. B. Liu
6. Erik Cambria
7. Furu Wei
8. Rada Mihalcea

Citations in the corpus

• Sentiment Classification of Reviews Using SentiWordNet (2009, result): “The author s argue that glosses have a potentially low level o f noise since they “are designed to match as close as possible the components of meaning of the word, have relatively standard style, grammar and syntact ic structure”; This idea is also seen in [5], this time by using supervised learnin”
• VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text (2014, methodology): “Like LIWC, the Harvard GI lexicon has been widely used in several works to automatically determine sentiment properties of text (Esuli & Sebastiani, 2005; Kamps, Mokken, Marx, & de Rijke, 2004; Turney & Littman, 2003).”
• Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text (2006, background): “…etc. Sentiment detection is the task of determining positive or negative sentiment of words (Hatzivassiloglou and McKeown, 1997; Turney, 2002; Esuli and Sebastiani, 2005), phrases and sentences (Kim and Hovy, 2004; Wilson et al ., 2005), or documents (Pang et al ., 2002; Turney, 2002).”
• Word Sense and Subjectivity (2006, methodology): “Second, a number of methods for subjectivity or sentiment analysis start with a set of seed words and then search through WordNet to find other subjective words (Kamps and Marx, 2002; Yu and Hatzivassiloglou, 2003; Hu and Liu, 2004; Kim and Hovy, 2004; Esuli and Sebastiani, 2005).”
• Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents (2007, methodology): “This method utilizes synonyms or glosses of a thesaurus in order to determine polarity of words (Kamps et al., 2004; Hu and Liu, 2004; Kim and Hovy, 2004; Esuli and Sebastiani, 2005).”
• A Survey of Opinion Mining and Sentiment Analysis (2012, background): “, machine learning) to generate better lists [1, 19, 20, 45].”
• A survey on sentiment detection of reviews (2009, background): “ Esuli and Sebastiani (2005) WordNet .874 10770 H. Tang et al. / Expert Systems with Applications 36 (2009) 10760–10773”
• Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach (2014, background): “Esuli and Sebastiani (2005) exploit the glosses information from Wordnet.”
• Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews (2006, background): “(2003), Yu and Hatzivassiloglou (2003), Kim and Hovy (2004), Gamon et al. (2005)), a phrase (e.g., Wilson et al. (2005)), and a specific object (such as a product) mentioned in a document (e.”
• Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis (2009, methodology): “…1997) and statistical measures of word association (Turney and Littman 2003), as well as techniques that exploit information about lexical relationships (Kamps and Marx 2002; Kim and Hovy 2004) and glosses (Esuli and Sebastiani 2005; Andreevskaia and Bergler 2006) in resources such as WordNet.”
• Helpfulness of Online Consumer Reviews: Readers' Objectives and Review Cues (2012, methodology): “For example, in review contents with “The Gillette Fusion is not worth the price,” “worth” is considered as a negative adjective, though it is viewed as a positive adjective according to SentiWordNet.”