Q-WordNet: Extracting Polarity from WordNet Senses

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Q-WordNet: Extracting Polarity from WordNet Senses
This paper presents Q-WordNet, a lexical resource consisting of WordNet senses automatically annotated by positive and negative polarity. Polarity classification amounts to decide whether a text (sense, sentence, etc.) may be associated to positive or negative connotations. Polarity classification is becoming important for applications such as Opinion Mining and Sentiment Analysis, which facilitates the extraction and analysis of opinions about commercial products, on companies reputation management, brand monitoring, or to track attitudes by mining online forums, blogs, etc. Inspired by work on classification of word senses by polarity (e.g., SentiWordNet), and taking WordNet as a starting point, we build Q-WordNet. Instead of applying external tools such as supervised classifiers to annotated WordNet synsets by polarity, we try to effectively maximize the linguistic information contained in WordNet, thereby taking advantage of the human effort put by lexicographers and annotators. T...
Rodrigo Agerri, Ana García-Serrano
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2010
Where LREC
Authors Rodrigo Agerri, Ana García-Serrano
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