|
Twitter
|
Facebook
|
Google+
|
VKontakte
|
LinkedIn
|
 
 
International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Monday 21 May 2018

About IJISR

News

Submission

Downloads

Archives

Custom Search

Contact

Connect with IJISR

  Call for Papers (May 2018)  
 
 
 

Neuro-Fuzzy Classification Techniques for Sentiment Analysis using Intelligent Agents on Twitter Data


Volume 23, Issue 2, May 2016, Pages 356–360

 Neuro-Fuzzy Classification Techniques for Sentiment Analysis using Intelligent Agents on Twitter Data

V. Soundarya and D. Manjula

Original language: English

Received 17 February 2016

Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


In this paper, we propose a new classification algorithm called Intelligent Agent and Neuro-Fuzzy Rule based Group Support Vector Machines (IGSVM) to perform major classification of sentiments and to form groups based on the sentiments of people with respect to change in time and place. Finally, the groups are used to form discussion forums on various topics including business, e-learning, tour and sports. The main advantage of the proposed work is to identify the user interest based on the sentiments identified from tweets and to form similar interest users groups for discussion on specific topics. From the experiments conducted in this work, it is proved that the user groups formed by sentiment analysis provided more than 94% accuracy in identifying members for forming interest groups on twitter and hence is more accurate than the existing systems.

Author Keywords: Sentiment classification, sentiment analysis, feature selection, Intelligent Group SVM, twitter.


How to Cite this Article


V. Soundarya and D. Manjula, “Neuro-Fuzzy Classification Techniques for Sentiment Analysis using Intelligent Agents on Twitter Data,” International Journal of Innovation and Scientific Research, vol. 23, no. 2, pp. 356–360, May 2016.