IITP:Supervised Machine Learning for Aspect based Sentiment Analysis

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 classification was not evaluated. However we evaluate the submitted system with the same evaluation metrics, and it shows the accuracies of 67.37 % and 67.07 % for the restaurants and laptops reviews, respec-tively.