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Towards Enhanced Opinion Classification using NLP TechniquesAuthors: Akshat Bakliwal,Piyush Arora,Ankit Patil,Vasudeva Varma Conference: 5th International Joint Conference on Natural Language Processing (IJCNLP) 2011, Location Chiang Mai, Thailand. Date: 2011-11-08 Report no: IIIT/TR/2011/70 AbstractSentiment 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 al- gorithm 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 . Full paper: pdf Centre for Search and Information Extraction Lab |
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