IEEE Transactions on Pattern Analysis and Machine Intelligence
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
An empirical study of sentiment analysis for chinese documents
Expert Systems with Applications: An International Journal
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
Introduction to Information Retrieval
Introduction to Information Retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A Feature Selection Method Based on Fisher's Discriminant Ratio for Text Sentiment Classification
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Selecting Attributes for Sentiment Classification Using Feature Relation Networks
IEEE Transactions on Knowledge and Data Engineering
Comparison of feature selection methods for sentiment analysis
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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Sentiment Analysis (SA) research has increased tremendously in recent times. Sentiment analysis deals with the methods that automatically process the text contents and extract the opinion of the users. In this paper, unigram and bi-grams are extracted from the text, and composite features are created using them. Part of Speech (POS) based features adjectives and adverbs are also extracted. Information Gain (IG) and Minimum Redundancy Maximum Relevancy (mRMR) feature selection methods are used to extract prominent features. Further, effect of various feature sets for sentiment classification is investigated using machine learning methods. Effects of different categories of features are investigated on four standard datasets i.e. Movie review, product (book, DVD and electronics) review dataset. Experimental results show that composite features created from prominent features of unigram and bi-gram perform better than other features for sentiment classification. mRMR is better feature selection method as compared to IG for sentiment classification. Boolean Multinomial Naïve Bayes (BMNB) algorithm performs better than Support Vector Machine (SVM) classifier for sentiment analysis in terms of accuracy and execution time.