Determining Term Subjectivity and Term Orientation for Opinion Mining

By Andrea Esuli, Fabrizio Sebastiani. Conference of the European Chapter of the Association for Computational Linguistics, 2006

347 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/af5c4034493461af13a7f5480e081becf0218511

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. Rada Mihalcea
3. Erik Cambria
4. Saif M. Mohammad
5. Janyce Wiebe
6. Bo Pang
7. Theresa Wilson
8. B. Liu

Citations in the corpus

• SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining (2006, methodology): “The effectiveness results reported in (Esuli and Sebastiani, 2006) may thus be considered only approximately indicative of the accuracy of the SENTIWORDNET labelling.”
• Learning Multilingual Subjective Language via Cross-Lingual Projections (2007, background): “, 2005), text semantic analysis (Wiebe and Mihalcea, 2006; Esuli and Sebastiani, 2006), and question answering (Yu and Hatzivassiloglou, 2003).”
• 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.”
• A survey on sentiment detection of reviews (2009, background): “Its basic assumption is that if a word is semantically oriented in one direction, then the words in its gloss tend to be oriented in the same direction (Esuli & Sebastiani, 2005; Esuli & Sebastiani, 2006a, 2006b).”
• Sentiment Analysis and Subjectivity (2010, background): “, machine learning) to generate better lists [1, 21, 22, 24, 43].”
• Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents (2007, background): “Esuli et al. utilized glosses of words to determine polarity (Esuli and Sebastiani, 2005; Esuli and Sebastiani, 2006).”
• A Survey of Opinion Mining and Sentiment Analysis (2012, background): “, machine learning) to generate better lists [1, 19, 20, 45].”
• Opinion Mining and Sentiment Analysis (2008, methodology): “Each synset of WordNet [95], a publicly available thesaurus-like resource, is assigned one of three sentiment scores — positive, negative, or objective — where these scores were automatically generated using a semi-supervised method described in Esuli and Sebastiani [90].”
• Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis (2009, background): “Esuli and Sebastiani (2006a) is the only work in prior-polarity identification to include a neutral (objective) category and to consider a three-way classification between positive, negative, and neutral words.”
• Semi-Supervised Polarity Lexicon Induction (2009, background): “These terms and prior knowledge of their polarity could be used as features in a supervised classification framework to determine the sentiment of the opinionated text (E.g., (Esuli and Sebastiani, 2006)).”
• Multilingual Subjectivity Analysis Using Machine Translation (2008, methodology): “, 2005), text semantic analysis (Wiebe and Mihalcea, 2006; Esuli and Sebastiani, 2006), tracking sentiment timelines in on-line forums and news (Lloyd et al.”
• Aspect-based sentiment analysis of movie reviews on discussion boards (2010, methodology): “SentiWordNet is a lexical resource for sentiment analysis, and its sentiment scores were automatically calculated using a semi-supervised method described in [6].”