Performance analysis of sentiments in twitter dataset using SVM models

Lakshmana Kumar Ramasamy, Seifedine Kadry, Yunyoung Nam


Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The twitter data set is analyzed using svm classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of svm radial kernel, svm linear grid and svm Radial Grid was compared and found that svm linear grid performs better than other svm models.


machine learning; sentiment analysis; svm model; twitter dataset;

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ISSN 2088-8708, e-ISSN 2722-2578