Learning Subjective Adjectives from Corpora

By Janyce Wiebe. AAAI/IAAI, 2000

615 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5581992944c66522dd1b11f8a6150aeef2d95b7a

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Citations in the corpus

Affective News: The Automated Coding of Sentiment in Political Texts (2012, background): “For examples in computational linguistics, see, for instance, Généreux and Evans (2006); Hatzivassiloglou and McKeown (1997); Joachims (1998); Kim and Hovy (2006); Kushal, Lawrence, and Pennock (2003); Leshed and Kaye (2006); Mishne (2005); Pang et al. (2002); and Wiebe (2000).”
Just How Mad Are You? Finding Strong and Weak Opinion Clauses (2004, background): “Clues learned from annotated data include distributionally similar adjectives and verbs (Wiebe, 2000) and n-grams (Dave et al., 2003; Wiebe et al., 2001).”
Identifying Collocations for Recognizing Opinions (2001, methodology): “In addition, in the current setting, there are no a priori values to use for parameters C (cluster size) and FT (filtering threshold), as there were in (Wiebe, 2000), and results vary with different parameter settings.”
Large-Scale Sentiment Analysis for News and Blogs (system demonstration) (2007, background): “Wiebe [8] evaluates adjectives for polarity as well as gradation classification.”
Word Sense and Subjectivity (2006, methodology): “(Wiebe, 2000) also makes use of an annotated corpus, but in a different approach: given a word w and a set of distributionally similar words DSW, that method assigns a subjectivity score to w equal to the conditional probability that any member of DSW is in a subjective expression.”
Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques (2003, background): “[22] identifies subjective adjectives (or sentiment adjectives) from corpora.”
FactBank: a corpus annotated with event factuality (2009, background): “Opinion identification can be performed at different levels of granularity: document (Pang et al. 2002; Turney 2002), clausal and phrasal (e.g., Wiebe et al. 2005; Read et al. 2007; Stoyanov and Cardie 2008), or even lexical (e.g., Wiebe 2000; Riloff et al. 2003; Andreevskaia and Bergler 2006).”
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews (2002, background): “Past work has demonstrated that adjectives are good indicators of subjective, evaluative sentences (Hatzivassiloglou & Wiebe, 2000; Wiebe, 2000; Wiebe et al., 2001).”
Twitter Sentiment Analysis (2015, background): “The ﬁeld of text categorization was initiated long time ago (Salton and McGill, 1983), however categorization based on sentiment was introduced more recently in (Das and Chen, 2001; Morinaga et al., 2002; Pang et al., 2002; Tong, 2001; Turney, 2002; Wiebe, 2000).”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “These can be strong indicators of sentiment in social media, and in fact are sometimes used to create noisy training data for sentiment analysis in Twitter (Pak and Paroubek, 2010; Kouloumpis et al., 2011).”
Select-additive learning: Improving generalization in multimodal sentiment analysis (2016, background): “the issue of confounding factors, followed by a discussion of our experimental results and conclusions. 1 Related Work For the text modality, sentiment analysis has been carried out at the word level [4,29], phrase level [30], sentences level [22] and document level [17] extensively. Recently, deep ”
Compositional Matrix-Space Models for Sentiment Analysis (2011, background): “There has been a lot of research in determining the sentiment of words and constructing polarity dictionaries (Hatzivas-siloglou and McKeown, 1997; Wiebe, 2000; Rao and Ravichandran, 2009; Mohammad et al., 2009; Velikovich et al., 2010).”
Opinion Mining and Sentiment Analysis (2008, unknown): “orientationin the literature) or subjectivity status [12, 45, 89, 90, 91, 92, 119, 131, 143, 146, 258, 287, 289, 290, 291, 300, 304, 306].”
Sentiment Analysis using Support Vector Machines with Diverse Information Sources (2004, background): “…emotive tone (Turney and Littman, 2003; Turney, 2002; Kamps et al., 2002; Hatzivassiloglou and Wiebe, 2000; Hatzi-vassiloglou and McKeown, 2002; Wiebe, 2000), but in many domains of text, the values of individual phrases may bear little relation to the over-all sentiment expressed by the text.”
Learning Subjective Language (2004, methodology): “The first study listed above (Turney 2002) uses a variation of the semantic similarity procedure presented in Wiebe (2000) (Section 3.4).”
A survey on sentiment detection of reviews (2009, methodology): “…has been performed and proposed for discriminating between subjective and objective text at the document and sentence levels (Bruce & Wiebe, 1999; Finn, Kushmerick, & Smyth, 2002; Hatzivassiloglou & Wiebe, 2000; Wiebe, 2000; Wiebe et al., 1999; Wiebe et al., 2002; Yu & Hatzivassiloglou, 2003).”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “Shallow syntactic features and lexical subjectivity clues have been retrieved using 4 different existing works by Hatzivassiloglou [31], Wiebe [87], Rilo et al. [153] i.e. MetaBoot algorithm, and Thelen et al. [83] i.e. Basilisk algorithm.”
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys (2010, background): “Several works (Wiebe, 2000; Turney, 2002; Riloff, 2003; Whitelaw et al., 2005) use lexical resources and decide whether a sentence expresses a sentiment by the presence of lexical items (sentiment words).”
Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, background): “The term sentiment analysis first appeared in [4], however the research on sentiments/opinions appeared earlier [5; 6; 7; 8; 9].”