Towards Enhanced Opinion Classification using NLP Techniques.
Akshat Bakliwal, Piyush Arora, Ankit Patil, Vasudeva Varma · 2011
Sentiment mining and classification plays an important role in predicting what people think about products, places, etc. In this piece of work, using basic NLP Techniques like NGram, POS-Tagged NGram we classify movie and product reviews broadly into two polarities: Positive and Negative. We propose a model to address the problem of determining whether a review is positive or negative, we experiment and use several machine learning algorithms Naive Bayes (NB), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) to have a comparative study of the performance of the method we devised in this work. Along with this we also did negation handling and observed improvements in classification. The algorithm we proposed achieved an average accuracy of 78.32 % on movie and 70.06% on multi-category dataset. In this paper we focus on the collective study of Ngram and POS tagged information available in the reviews. 1