Penguin Search Optimization Based Feature Selection for Automated Opinion Mining
ST. Anuprathibha1, C. S. KanimozhiSelvi2

1T. Anuprathibha*, Research scholar, Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu- 638060, India.
2Dr. C. S. KanimozhiSelvi, Associate Professor, Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamil Nadu India. 

Manuscript received on 7 August 2019. | Revised Manuscript received on 14 August 2019. | Manuscript published on 30 September 2019. | PP: 648-653 | Volume-8 Issue-3 September 2019 | Retrieval Number: B2629078219/19©BEIESP | DOI: 10.35940/ijrte.B2629.098319
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Abstract: Twitter sentiment analysis is a vital concept in determining the public opinions about products, services, events or personality. Analyzing the medical tweets on a specific topic can provide immense benefits in medical industry. However, the medical tweets require efficient feature selection approach to produce significantly accurate results. Penguin search optimization algorithm (PeSOA) has the ability to resolve NP-hard problems. This paper aims at developing an automated opinion mining framework by modeling the feature selection problem as NP-hard optimization problem and using PeSOA based feature selection approach to solve it. Initially, the medical tweets based on cancer and drugs keywords are extracted and pre-processed to filter the relevant informative tweets. Then the features are extracted based on the Natural Language Processing (NLP) concepts and the optimal features are selected using PeSOA whose results are fed as input to three baseline classifiers to achieve optimal and accurate sentiment classification. The experimental results obtained through MATLAB simulations on cancer and drug tweets using k-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) indicate that the proposed PeSOA feature selection based tweet opinion mining has improved the classification performance significantly. It shows that the PeSOA feature selection with the SVM classifier provides superior sentiment classification than the other classifiers.
Keywords: Natural Language Processing, Opinion Mining, Penguin Search Optimization Algorithm, Sentiment Analysis, Twitter.

Scope of the Article: Natural Language Processing