Indian_Institute_of_Technology-Patna: Sentiment Analysis in Twitter

Abstract

This paper is an overview of the system submitted to the SemEval-2014 shared task on sentiment analysis in twitter. For the very first time we participated in both the tasks, viz contextual polarity disambiguation and message polarity classification. Our approach is supervised in nature and we use sequential minimal optimization classifier. We implement the features for sentiment analysis without using deep domain-specific resources and/or tools. Experiments within the benchmark setup of SemEval-14 shows the F-scores of 77.99%, 75.99%, 76.54 %, 76.43% and 71.43% for LiveJournal2014, SMS2013, Twitter2013, Twitter2014 and Twitter2014Sarcasm, respectively for Subtask A. For Subtask B we obtain the F-scores of 60.39%, 51.96%, 52.58%, 57.25%, 41.33% for five different test sets, respectively.