Paper: Kea: Sentiment Analysis of Phrases Within Short Texts

ACL ID S14-2065
Title Kea: Sentiment Analysis of Phrases Within Short Texts
Venue Joint Conference on Lexical and Computational Semantics
Year 2014

Sentiment Analysis has become an in- creasingly important research topic. This paper describes our approach to building a system for the Sentiment Analysis in Twit- ter 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 do- mains quite effectively. However, in cross- domain experiments, SMS data is found to generalize even better than Twitter data.