Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis

By Hiroshi Kanayama, Tetsuya Nasukawa. Conference on Empirical Methods in Natural Language Processing, 2006

479 citations recorded by the source corpus.

This paper proposes an unsupervised lexicon building method for the detection of polar clauses, which convey positive or negative aspects in a specific domain. The lexical entries to be acquired are called polar atoms, the minimum human-understandable syntactic structures that specify the polarity of clauses. As a clue to obtain candidate polar atoms, we use context coherency, the tendency for same polarities to appear successively in contexts. Using the overall density and precision of coherency in the corpus, the statistical estimation picks up appropriate polar atoms among candidates, without any manual tuning of the threshold values. The experimental results show that the precision of polarity assignment with the automatically acquired lexicon was 94% on average, and our method is robust for corpora in diverse domains and for the size of the initial lexicon.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/8ec5c6abf7b8c64d5939285bfe97b56d9bd2c6f4

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In Artificial Intelligence

Citations in the corpus

A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “Nasukawa and Yi [171] performed subject favorability determination by creating a sentiment lexicon of 3513 sentiment terms.”
A Survey of Opinion Mining and Sentiment Analysis (2012, background): “In [46], Kanayama and Nasukawa expanded this approach by introducing the idea of intra-sentential (within a sentence) and intersentential (between neighboring sentences) sentiment consistency (called coherency in [46]).”
Multilingual Subjectivity Analysis Using Machine Translation (2008, methodology): “…been applied to English data, though work on other languages is growing: e.g., Japanese data are used in (Kobayashi et al., 2004; Suzuki et al., 2006; Takamura et al., 2006; Kanayama and Nasukawa, 2006), Chinese data are used in (Hu et al., 2005), and German data are used in (Kim and Hovy, 2006).”
A holistic lexicon-based approach to opinion mining (2008, background): “Most sentence level and even document level classification methods are based on identification of opinion words or phrases.”
Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents (2007, background): “(Kanayama and Nasukawa, 2006) reported that it was appropriate in 72.2% of cases.”
Opinion Mining and Sentiment Analysis (2008, background): “WordNet-defined relations, or other related words (and, along the same lines, opposite labels can be given based on similar information) [12, 20, 89, 90, 131, 146, 148, 155, 289, 299, 300].”
Mining Opinions in Comparative Sentences (2008, background): “Discovering orientations of context dependent opinion comparative words is related to identifying domain opinion words (Hatzivassiloglou and McKeown 1997; Kanayama and Nasukawa 2006).”
A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “…(Pang et al. 2002;Moraes et al. 2013; Saleh et al. 2011; Habernal et al. 2015; Riaz et al. 2017; Wang et al. 2017a), lexicon-based approaches (Kanayama and Nasukawa 2006; Dang et al. 2010; Pandarachalil et al. 2015; Saif et al. 2016; Taboada et al. 2011; Turney 2002; Molina-González et al.…”
Learning Multilingual Subjective Language via Cross-Lingual Projections (2007, methodology): “, Japanese data are used in (Takamura et al., 2006; Kanayama and Nasukawa, 2006), Chinese data are used in (Hu et al.”
Opinion Word Expansion and Target Extraction through Double Propagation (2011, methodology): “…the existing work can be categorized as corpora-based (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…”
Learning Sentiment Lexicons in Spanish (2012, background): “…applied to English, but work on other languages is growing, including Japanese (Kobayashi et al., 2004; Suzuki et al., 2006; Takamura et al., 2006; Kanayama and Nasukawa, 2006), Chinese (Hu et al., 2005; Tsou et al., 2005; Zagibalov and Carroll, 2008), German (Kim and Hovy, 2006), and Romanian…”
Aspect-Oriented Opinion Mining from User Reviews in Croatian (2013, background): “Most approaches to domainspecific sentiment lexicon acquisition start from a manually compiled set of aspects and opinion clues and then expand it with words satisfying certain co-occurrence or syntactic criteria in a domainspecific corpus (Kanayama and Nasukawa, 2006; Popescu and Etzioni, 2007; Fah”
Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis (2009, background): “…Esuli and Sebastiani 2005; Takamura, Inui, and Okumura 2005; Popescu and Etzioni 2005; Andreevskaia and Bergler 2006; Esuli and Sebastiani 2006a; Kanayama and Nasukawa 2006) to characterizing the sentiment of documents, such as recognizing inﬂammatory messages (Spertus 1997), tracking sentiment…”