OPTWIMA: Comparing Knowledge-rich and Knowledge-poor Approaches for Sentiment Analysis in Short Informal Texts
Alexandra Balahur · 2013
The fast development of Social Media made it possible for people to no loger remain mere spectators to the events that happen in the world, but become part of them, comment-ing on their developments and the entities in-volved, sharing their opinions and distribut-ing related content. This phenomenon is of high importance to news monitoring systems, whose aim is to obtain an informative snap-shot of media events and related comments. This paper presents the strategies employed in the OPTWIMA participation to SemEval 2013 Task 2-Sentiment Analysis in Twitter. The main goal was to evaluate the best settings for a sentiment analysis component to be added to the online news monitoring system. We describe the approaches used in the com-petition and the additional experiments per-formed combining different datasets for train-ing, using or not slang replacement and gener-alizing sentiment-bearing terms by replacing them with unique labels. The results regarding tweet classification are promising and show that sentiment generaliza-tion can be an effective approach for tweets and that SMS language is difficult to tackle, even when specific normalization resources are employed. 1