Kea: Sentiment Analysis of Phrases Within Short Texts

Sentiment Analysis has become an increasingly important research topic. This paper describes our approach to building a system for the Sentiment Analysis in Twitter task of the SemEval-2014 evaluation. The goal is to classify a phrase within a short piece of text as positive, negative or neutral. In the evaluation, classifiers trained on Twitter data are tested on data from other domains such as SMS, blogs as well as sarcasm. The results indicate that apart from sarcasm, classifiers built for sentiment analysis of phrases from tweets can be generalized to other short text domains quite effectively. However, in crossdomain experiments, SMS data is found to generalize even better than Twitter data.