Robustious Feature Selection Based Genetic Algorithm (RFS-GA) For Cross Domain Opinion Mining
E. Chandra Blessie1, S. Gnanapriya2 

1Dr. E. Chandra blessie, Associate Professor, Department of Computer Applications, Nehru College of Management, Coimbatore, (Tamil Nadu), India. 
2S. Gnanapriya, Ph. D Research Scholar, Department of Computer Applications, Nehru College of Management, Coimbatore, (Tamil Nadu), India.

Manuscript received on 10 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 6267-6279 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3199078219/2019©BEIESP | DOI: 10.35940/ijrte.B3199.078219
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Abstract: Day by day the requirement of information for processing the sentiment analysis is getting increased multiple times. For these kinds of reasons, feature selection is utilized to detect the opinion among different reviews and comments. Sentiment analysis is becoming like phenomenon due to increase of social media’s popularity. Currently, significant advancements are shown in this research domain, but still multiple challenges are to be solved – i.e., sentiment analysis in cross domains. In this paper rumbustious feature selection based genetic algorithm is proposed to address the problem of analyzing the sentiments in cross domain. It performs classification based optimistic-class and pessimistic-class. The dataset used to this research work includes books, DVDs, gadgets and kitchen appliances. Initially the features selection is performed and opinion mining is performed by Genetic Algorithm. Benchmark performance metrics are selected for measuring the performance of proposed work against existing method. Results depict that the proposed work has better performance than that of the existing work as far as chosen performance metrics.
Keywords: Web Mining, Opinion Mining, Sentiment Analysis, Feature Selection, Genetic Algorithm
Scope of the Article: Web Mining