Mining WordNet for a Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses

By Alina Andreevskaia, Sabine Bergler. Conference of the European Chapter of the Association for Computational Linguistics, 2006

345 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d2d8b52f59945b4a3ef9d20ab44e108319eead6f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Bing Liu
2. Erik Cambria
3. Duyu Tang
4. B. Liu
5. Yejin Choi
6. Soujanya Poria
7. Richard Socher
8. Saif M. Mohammad

Citations in the corpus

• A survey on sentiment detection of reviews (2009, background): “Andreevskaia and Bergler (2006) proposed an algorithm named ''STEP " (Semantic Tag Extraction Program).”
• Sentiment Analysis and Opinion Mining (2012, background): “Sentence (5) expresses a negative opinion about the weight of the camera.”
• Large-Scale Sentiment Analysis for News and Blogs (system demonstration) (2007, background): “• We associate a polarity (positive or negative) to each word and query both the synonyms and antonyms, akin to [15, 16] Synonyms inherit the polarity from the parent, whereas antonyms get the opposite polarity.”
• Unsupervised sentiment analysis with emotional signals (2013, methodology): “The second is dictionary-based methods [2, 26], which employ a dictionary, e.”
• A Survey of Opinion Mining and Sentiment Analysis (2012, background): “, machine learning) to generate better lists [1, 19, 20, 45].”
• Contextual semantics for sentiment analysis of Twitter (2016, methodology): “To reduce the cost of building these sentiment lexicons, some approaches apply bootstrapping techniques to add words to an initial subset or seeds (Andreevskaia and Bergler, 2006; Neviarouskaya et al., 2011).”
• Learning Multilingual Subjective Language via Cross-Lingual Projections (2007, background): “This is reported in studies of manual annotation of phrases (Takamura et al., 2006), recognizing contextual polarity of expressions (Wilson et al., 2005), and sentiment tagging of words and word senses (Andreevskaia and Bergler, 2006; Esuli and Sebastiani, 2006).”
• SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining (2006, background): “…indicates widespread agreement among different automated classifiers; in essence, this is the same observation which has independently been made in (Andreevskaia and Bergler, 2006), where agreement among human classifiers is shown to correlate strongly with agreement among automated classifiers,…”
• Learning Word Vectors for Sentiment Analysis (2011, methodology): “Thus, we extend the model with a supervised sentiment component that is capable of embracing many social and attitudinal aspects of meaning (Wilson et al., 2004; Alm et al., 2005; Andreevskaia and Bergler, 2006; Pang and Lee, 2005; Goldberg and Zhu, 2006; Snyder and Barzilay, 2007).”
• Connotation Frames: A Data-Driven Investigation (2015, background): “Most prior work on sentiment 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; Adre”
• Sentiment Analysis and Subjectivity (2010, background): “, machine learning) to generate better lists [1, 21, 22, 24, 43].”
• Affective News: The Automated Coding of Sentiment in Political Texts (2012, background): “…researchers adopting this approach have thus calculated the degree of centrality to a category by (manually or statistically) weighing a word according to all possible meanings, magnitude or intensity, or lexical relations with other words (Andreevskaia & Bergler, 2006; Subasic & Huettner, 2001).”
• 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 ).”
• 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.”
• Opinion Mining and Sentiment Analysis (2008, background): “[209] summarize the evidence of several projects on subsentential analysis [12, 90, 290, 320] as follows: “the problem of distinguishing subjective versus objective instances has often proved to be more difficult than subsequent polarity classification, so improvements in subjectivity classification”
• Sentiment Classification of Reviews Using SentiWordNet (2009, methodology): “In [2] a method for lexicon expansion is proposed where terms are assigned positive or negative opini ns based on the existence of terms known to carry opinion content found on the term gloss.”