The shared task on Aspect based Sentiment Analysis primarily focuses on mining relevant information from the thousands of online reviews available for a popular product or service. In this paper we report 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 classifiers, and finally combine their outputs using a majority voting technique. For sentiment classification we use Random Forest classifier. Our system for aspect term extraction shows the F-scores of 72.13% and 62.84% for the restaurants and laptops reviews, respectively. Due to some technical 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, respectively.
Deepak Kumar Gupta REF proposed a ensemble system using different classifiers and combined them by using a majority voting technique.
IITP: Supervised Machine Learning for Aspect based Sentiment Analysis
SemEval@COLING
Computer Science