Paper: IITP: Supervised Machine Learning for Aspect based Sentiment Analysis

ACL ID S14-2053
Title IITP: Supervised Machine Learning for Aspect based Sentiment Analysis
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

The shared task on Aspect based Senti- ment Analysis primarily focuses on mining relevant information from the thousands of online reviews available for a popular product or service. In this paper we re- port our works on aspect term extraction and sentiment classification with respect to our participation in the SemEval-2014 shared task. The aspect term extraction method is based on supervised learning algorithm, where we use different classi- fiers, and finally combine their outputs us- ing a majority voting technique. For senti- ment classification we use Random Forest classifier. Our system for aspect term ex- traction shows the F-scores of 72.13% and 62.84% for the restaurants and laptops re- views, respectively. Due to some techni- cal problems our submission on sentiment classificat...