Detecting Entity-Related Events and Sentiments from Tweets Using Multilingual Resources

This article presents the details of the participation of the OPTAH team to the CLEF 2012 RepLab profiling (polarity classification) and monitoring tasks. Specifically, we present the manner in which the OPAL system has been modified to deal with opinions in tweets and how the use of rules involving the language use in social-media can help to achieve good results as far as polarity classification is concerned, even in a language for which we have just a small polarity lexicon. Additionally, we show how we can employ the values computed for sentiment intensity (especially the negative ones) to classify the importance of event-related clusters of tweets. Our methods, although quite simple, obtained promising results in the RepLab evaluations.