Automatic, Adaptive, and Applicative Sentiment Analysis

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Post on 26-Oct-2014

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Sentiment analysis is a challenging task today for computational linguistics. Because of the rise of the social Web, both the research and the industry are interested in automatic processing of opinions in text. In this work, we assume a multilingual and multidomain environment and aim at automatic and adaptive polarity classification. In particular, we propose a method for automatic construction of multilingual affective lexicons from microblogging to cover the lack of lexical resources. We propose a novel text representation model based on dependency parse trees to replace a traditional n-grams model. Finally, we investigate the impact of entity-specific features on classification of minor opinions and propose normalization schemes for improving polarity classification. The effectiveness of our approach has been proved in experimental evaluations that we have performed across multiple domains (movies, product reviews, news, blog posts) and multiple languages (English, French, Russian, Spanish, Chinese) including official participation in several international evaluation campaigns (SemEval'10, ROMIP'11, I2B2'11).

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