Extracting Semantic Orientations of Words using Spin Model

By Hiroya Takamura, Takashi Inui, Manabu Okumura. Annual Meeting of the Association for Computational Linguistics, 2005

406 citations recorded by the source corpus.

We propose a method for extracting semantic orientations of words: desirable or undesirable. Regarding semantic orientations as spins of electrons, we use the mean field approximation to compute the approximate probability function of the system instead of the intractable actual probability function. We also propose a criterion for parameter selection on the basis of magnetization. Given only a small number of seed words, the proposed method extracts semantic orientations with high accuracy in the experiments on English lexicon. The result is comparable to the best value ever reported.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/16a2920fa4d6a0d3ab2b5c8c3a08d1008cdcfeb3

How researchers see this paper

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In Artificial Intelligence

1. B. Liu
2. Yejin Choi
3. Bing Liu
4. Lei Zhang
5. Erik Cambria
6. Janyce Wiebe
7. Luke S. Zettlemoyer
8. Noah A. Smith

Citations in the corpus

• Opinion Word Expansion and Target Extraction through Double Propagation (2011, background): “…(Hatzivassiloglou and McKeown 1997; Wiebe 2000; Wiebe et al. 2004; Turney and Littman 2003; Kanayama and Nasukawa 2006; Kaji and Kitsuregawa 2007) and dictionary-based (Hu and Liu 2004; Kim and Hovy 2004; Kamps et al. 2004; Esuli and Sebastiani 2005; Takamura, Inui, and Okumura 2005) approaches.”
• Phrase Dependency Parsing for Opinion Mining (2009, background): “Amount of works have been done on sentimental classification in different levels (Zhang et al., 2009; Somasundaran et al., 2008; Pang et al., 2002; Dave et al., 2003; Kim and Hovy, 2004; Takamura et al., 2005).”
• Sentiment Analysis and Opinion Mining (2012, methodology): “Other methods based on graphs include those in Takamura et al. (2005, 2006, 2007).”
• A survey on sentiment detection of reviews (2009, background): “However, more researches showed most of the adjectives and adverb, a small group of nouns and verbs possess semantic orientation (Andreevskaia & Bergler, 2006; Esuli & Sebastiani, 2005; Gamon & Aue, 2005; Takamura, Inui, & Okumura, 2005; Turney & Littman, 2003).”
• Extracting Product Features and Opinions from Reviews (2005, background): “Most recently, (Takamura et al., 2005) reports on the use of spin models to infer the semantic orientation of words.”
• Opinion Extraction, Summarization and Tracking in News and Blog Corpora (2006, methodology): “A statistical model is used for sentiment words too, but the experiment material is not described in detail (Takamura et al ., 2005).”
• Connotation Frames: A Data-Driven Investigation (2015, background): “…lexicons focused on the overall polarity of words without taking into account their semantic arguments (Wilson et al., 2005; Baccianella et al., 2010; Wiebe et al., 2005; Velikovich et al., 2010; Kaji and Kitsuregawa, 2007; Kamps et al., 2004; Takamura et al., 2005; Adreevskaia and Bergler, 2006).”
• Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents (2007, background): “Takamura et al. built lexical network from not only such co-occurrence but other resources including thesaurus (Takamura et al., 2005).”
• Contextual semantics for sentiment analysis of Twitter (2016, methodology): “, 2010; Wittgenstein, 1953, 2001), and have often been used for sentiment analysis (Turney, 2002; Turney and Littman, 2003; Takamura et al., 2005).”
• Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis (2009, background): “Takamura et al. (2005) use negation words and phrases, including phrases such as lack of that are members in our lists of polarity shifters, and conjunctive expressions that they collect from corpora.”