Paper: DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in Twitter

ACL ID S14-2035
Title DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in Twitter
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes our participation at SemEval- 2014 sentiment analysis task, in both contextual and message polarity classification. Our idea was to com- pare two different techniques for sentiment analysis. First, a machine learning classifier specifically built for the task using the provided training corpus. On the other hand, a lexicon-based approach using natural language processing techniques, developed for a ge- neric sentiment analysis task with no adaptation to the provided training corpus. Results, though far from the best runs, prove that the generic model is more robust as it achieves a more balanced evaluation for message polarity along the different test sets.