The benefit of concept-based features for sentiment analysis

Sentiment analysis is an active field of research, moving from the traditional algorithms that operated on complete documents to finegrained variants where aspects of the topic being discussed are extracted, as well as their associated sentiment. Recently, a move from traditional word-based approaches to concept-based approaches has started. In this work, it is shown by using a simple machine learning baseline, that concepts are useful as features within a machine learning framework. In all our experiments, the performance increases when including the conceptbased features.

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