Building Affective Lexicons from Specific Corpora for Automatic Sentiment Analysis

Abstract

Automatic sentiment analysis in texts has attracted considerable attention in recent years. Most of the approaches developed to classify texts or sentences as positive or negative rest on a very specific kind of language resource: emotional lexicons. To build these resources, several automatic techniques have been proposed. Some of them are based on dictionaries while others use corpora. One of the main advantages of the corpora techniques is that they can build lexicons that are tailored for a specific application simply by using a specific corpus. Currently, only anecdotal observations and data from other areas of language processing plead in favour of the utility of specific corpora. This research aims to test this hypothesis. An experiment based on 702 sentences evaluated by judges shows that automatic techniques developed for estimating the valence from relatively small corpora are more efficient if the corpora used contain texts similar to the one that must be evaluated.

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