Open Access Open Access  Restricted Access Subscription or Fee Access

Combining the Classification of Sentiments from Opinions

J. Soparia, A. Thakkar


Our day-to-day life has been influenced by varied perceptions of people. Their ideas and opinions have affected our own opinions. People give their own views on the particular product or services according to their usage application. Updating of data is necessary with every passing day which results into difficulty of examining and understanding these large amounts of data. So, it becomes difficult to examine all the varied opinions and come to a decision. This has motivated an interest in sentiment analysis and opinion in the recent years. If one user gives his opinion in sentence and the other gives in any star rating form, then this creates a limitation as it does not give any final conclusion to the opinion because the given opinions are in different forms. To overcome this limitation, we propose a method for aggregating the sentiments classified into different forms (Sentence opinion, Emoticons).

Cite this Article
Soparia and Thakkar. Combining the Classification of Sentiments from Opinions. Journal of Advanced Database Management & Systems. 2016; 3(1): 26–29p.


Internet movie database, online social network, self-organizing maps

Full Text:



Nandi G, Das A. A survey on using data mining techniques for online social network analysis. Int. J. Comput. Sci. Issues (IJCSI). 2013: 10(6): 162–167p.

Adedoyin-Olowe et al. A survey of data mining techniques for social media analysis. arXiv preprint arXiv. 2013; 1312: 4617p. 3. Hunter, David R, et al. ERGM: A package to fit, simulate and diagnose exponential-family models for networks. Journal of Statistical Software. 2008; 24(3): 548–60p.

Govindarajan M. Sentiment Classification of Movie Reviews Using Hybrid Method. International Journal of Advances in Science Engineering and Technology. 2013; 3(4): 139p.

Anitha N, Anitha B, Pradeepa S, et al. Sentiment Classification Approaches– A Review.

Puteri et al. Finding “interesting” trends in social networks using frequent pattern mining and self-organizing maps. Knowledge-Based Systems, Elsevier. 2012; 29: 104–113p. 7. Jagtap VS, Karishma P. Analysis of different approaches to sentence-level sentiment classification. International Journal of Scientific Engineering and Technology. 2013; 293: 164–170p. 8. Hogenboom et al. A statistical approach to star rating classification of sentiment. Management Intelligent Systems. Springer Berlin Heidelberg. 2012; 251–260p.

GeetikaV, Sangharsh T. Facebook as a Corpus for Emoticons-Based Sentiment Analysis. International Journal of

Emerging Technology and Advanced Engineering. 2014; 4(5): 904–908p.

Alexander [email protected], Daniella [email protected], Flavius [email protected], Malissa [email protected], Franciska de Jong1,2 [email protected], Uzay [email protected] ,1Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, the Netherlands, 2Universiteit Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands, 3Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, the Netherlands, Exploiting Emoticons in Sentiment Analysis.

Jayshri K, Mayur K. Machine Learning Algorithms for opinion mining and sentiment classification. International Journal of Scientific and Research Publications. Jun 2013; 3(6): 1–6p. 12. Scaria et al. Predicting Star Ratings of Movie Review Comments.


  • There are currently no refbacks.

This site has been shifted to