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Extracting and Grounding Context-Aware Sentiment Lexicons

Weichselbraun, Albert and Gindl, Stefan and Scharl, Arno (2013) Extracting and Grounding Context-Aware Sentiment Lexicons. IEEE Intelligent Systems, 28 (2). pp. 39-46.

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Official URL: http://dx.doi.org/10.1109/MIS.2013.41

Abstract

Web intelligence applications track online sources with economic relevance such as customer reviews, news articles and social media postings. Automated sentiment analysis based on lexical methods or machine learning identifies the polarity of opinions expressed in these sources to assess how stakeholders perceive a topic. This paper introduces a hybrid approach that combines the throughput of lexical analysis with the flexibility of machine learning to resolve ambiguity and consider the context of sentiment terms. The context-aware method identifies ambiguous terms that vary in polarity depending on the context and stores them in contextualized sentiment lexicons. In conjunction with semantic knowledge bases, these lexicons help ground ambiguous sentiment terms to concepts that correspond to their polarity. This grounding paves the way for interlinking, extending, or even replacing contextualized sentiment lexicons with semantic knowledge bases. An extensive evaluation applies the method to user reviews across three domains (movies, products and hotels).

Item Type:Article
Uncontrolled Keywords:Sentiment Analysis, Opinion Mining, Context-Aware Sentiment Analysis, Concept Analysis, Concept Grounding
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
ID Code:69
Deposited By: Dr Albert Weichselbraun
Deposited On:28 Jul 2013 04:08
Last Modified:28 Jul 2013 04:08

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