Identifying Collocations for Recognizing Opinions

By Janyce Wiebe, Theresa Wilson, Matthew Bell. 2001

168 citations recorded by the source corpus.

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Citations in the corpus

Text Understanding from Scratch (2015, background): “Many researchers have shown that with some random text, the inter-rater consensus on polarity is only about 60% - 80%(Gamon & Aue, 2005)(Kim & Hovy, 2004)(Strappa-rava & Mihalcea, 2008)(Viera et al., 2005)(Wiebe et al., 2001)(Wilson et al., 2005).”
Thumbs up? Sentiment Classification using Machine Learning Techniques (2002, background): “Other work explicitly attempts to find features indicating that subjective language is being used (Hatzivassiloglou and Wiebe, 2000; Wiebe et al., 2001).”
Learning Extraction Patterns for Subjective Expressions (2003, methodology): “…words (e.g., Levin’s desire verbs (1993)), and some empirical methods in NLP have automatically identiﬁed adjectives, verbs, and N-grams that are statistically associated with subjective language (e.g., (Turney, 2002; Hatzi-vassiloglou and McKeown, 1997; Wiebe, 2000; Wiebe et al., 2001)).”
Creating Subjective and Objective Sentence Classifiers from Unannotated Texts (2005, methodology): “For example, newspaper articles are typically thought to be relatively objective, but [12] reported that, in their corpus, 44% of sentences (in articles that are not editorials or reviews) were subjective.”
Annotating Expressions of Opinions and Emotions in Language (2005, background): “Document classification tasks include, for example, distinguishing editorials from news articles and classifying reviews as positive or negative ( Wiebe et al., 2001b; Pang et al., 2002, Yu and Hatzivassiloglou, 2003).”
Automatic Identification of Sentiment Vocabulary: Exploiting Low Association with Known Sentiment Terms (2005, background): “The field of sentiment classification has received considerable attention from researchers in recent years (Pang and Lee 2002, Pang et al. 2004, Tur-ney 2002, Turney and Littman 2002, Wiebe et al. 2001, Bai et al. 2004, Yu and Hatzivassiloglou 2003 and many others).”
Opinion Mining and Sentiment Analysis (2008, background): “[316], and a similar corpus was explored in previous work [309, 317].”
Learning Subjective Language (2004, methodology): “Another two-level annotation project was performed in Wiebe et al. (2001), this time involving document-level and expression-level annotations of newsgroup data (NG-FE in Table 1).”
Sentiment analysis: capturing favorability using natural language processing (2003, background): “• Features of expressions to be used for sentiment analysis such as collocations [12,14] and adjectives [5]”
Just How Mad Are You? Finding Strong and Weak Opinion Clauses (2004, methodology): “Finally, low-frequency words, which require no training to identify (Wiebe et al., 2001), are also used as clues.”
Learning subjective nouns using extraction pattern bootstrapping (2003, methodology): “For example, newspaper articles are typically thought to be relatively objective, but (Wiebe et al., 2001) reported that, in their corpus, 44% of sentences (in articles that are not editorials or reviews) were subjective.”
Opinion Extraction, Summarization and Tracking in News and Blog Corpora (2006, methodology): “Wiebe et al. (2002) proposed a method for opinion summarization by analyzing the relationships among basic opinionated units within a document.”
Mining the peanut gallery: opinion extraction and semantic classification of product reviews (2003, background): “” Substitutions have been used to solve these sorts of problems in subjectivity identification, text classification, and question answering [18, 19, 26], but, as indicated in Table 3, they are mostly ineffective for our task.”
Survey on mining subjective data on the web (2011, methodology): “Therefore, performing this identification task can be an effective addition to the classification step in order to improve precision (Wiebe et al. 2001; Dave et al. 2003; Pang and Lee 2004; Riloff et al. 2005; Wiebe and Riloff 2005; Wilson et al. 2005).”
A survey on sentiment detection of reviews (2009, unknown): “... Devitt & Vogel, 2004; Edmonds, 1999; Edmonds & Hirst, 2002; Fellbaum, 1998; Grefenstette, 1994; Justeson & Slava, 1993; Kamps & Maarten, 2002; Rapp, 2004; Riloff & Shepherd, 1997; Roark & Charniak, 1998; Taboada, 2006; Thelen & Riloff, 2002; Valitutti, Strapparava, & Stock, 2004), the other base”
The Statistics of Word Cooccur-rences: Word Pairs and Collocations (2004, background): “• contrastive cooccurrence data (obtained from different corpora or subcorpora) have been used for a variety of classification tasks, ranging from word sense disambiguation (Biber 1993; Justeson and Katz 1995b; Pedersen 2001; Resnik 1997; Rapp 2004) to the detection of topic shifts (Ferret 2002) and”
Contextual Valence Shifters (2006, background): “We argue that the current work in this area that concentrates mainly on the negative or positive attitude communicated by individual terms (Edmonds & Hirst, 2002; Hatzivassiloglou and McKeown, 1997; Turney & Littman, 2002; Wiebe et al., 2001) is incomplete and often gives the wrong results when impl”