Using an Emotion-based Model and Sentiment Analysis Techniques to Classify Polarity for Reputation.

Online Reputation Management is a novel and active area in Computational Linguistics. Closely related to opinion mining and sentiment analysis, it incorporates new features to traditional tasks like polarity detection. In this paper, we study the feasibility of applying complex sentiment analysis methods to classifying polarity for reputation. We adapt an existing emotional concept-based system for sentiment analysis to determine polarity of tweets with reputational information about companies. The original system has been extended to work with texts in English and in Spanish, and to include a module for filtering tweets according to their relevance to each company. The resulting UNED system for profiling task participated in the first RepLab campaign. The experimental results prove that sentiment analysis techniques are a good starting point for creating systems for automatic detection of polarity for reputation.