Paper: IITPatna: Supervised Approach for Sentiment Analysis in Twitter

ACL ID S14-2054
Title IITPatna: Supervised Approach for Sentiment Analysis in Twitter
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

In this paper we report our works for SemEval-2014 Sentiment Analysis in Twitter evaluation challenge. This is the first time we attempt for this task, and our submissions are based on supervised machine learning algorithm. We use Sup- port Vector Machine for both the tasks, viz. contextual polarity disambiguation and message polarity classification. We identify and implement a small set of features for each the tasks, and did not make use of any external resources and/or tools. The systems are tuned on the devel- opment sets and finally blind evaluation is performed on the respective test set, which consists of the datasets of five different domains. Our submission for the first task shows the F-score values of 76.3%, 77.04%, 70.91%, 72.25% and 66.32% for LiveJournal2014, SMS2013, Twitter...