A Swarm Intelligence Based Weighted Feature Extraction and Classification using SVM for Sentimental Exploration
P. V. Naga Srinivas1, M. V. P. Chandra Sekhara Rao2

1P. V. Naga Srinivas, Research scholar- CSE Department, University College of Engineering & Technology, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India.
2M. V. P. Chandra Sekhara Rao, Professor, CSE Department, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.

Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 883-890 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4077098319/19©BEIESP | DOI: 10.35940/ijrte.C4077.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The goal of Sentiment Exploration (SE) is used for mining the accurate sentiments which are very beneficial for businesses, governments, and individuals, the opinions, recommendations, ratings, and feedbacks are becoming an important aspect in present scenarios. The proposed methodology likewise attempts to introduce a swarm intelligence based sentimental supervised methodology. In order to obtain a relevant feature data set from a large number of data samples, this method used particle swarm optimization to attain the utmost optimum feature set. The evaluation of the optimum feature set is obtained by means of using Minimum Redundancy and Maximum Relevancy measure as the fitness function. The categorization of the extracted feature set is accomplished with the Support Vector Machine classification technique. The experimental outcome for the suggested method is evaluated using four performance measure like precision, recall, accuracy, and f-measure and showed that proposed swarm intelligent based classification method has better performance using IMDB, Movie Lens and Trip Advisor Data Samples.
Keywords: Sentimental Exploration, Particle Swarm Optimization, Minimum Redundancy Maximum Relevancy, Support Vector Machine, Classification, Feature Selection.

Scope of the Article: