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PERFORMANCE OF BIG DATA ANALYSIS OF SENTIMENTS IN TWITTER DATASET USING SVM MODELS |
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| รหัสดีโอไอ | |
| Creator | Preethi Nanjundan, K. Maheswari, Jayabrabu Ramakrishnan, Dinesh Mavalur, Azath Mubarakali, S. Ramkumar |
| Title | PERFORMANCE OF BIG DATA ANALYSIS OF SENTIMENTS IN TWITTER DATASET USING SVM MODELS |
| Contributor | - |
| Publisher | TuEngr Group |
| Publication Year | 2563 |
| Journal Title | International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies |
| Journal Vol. | 11 |
| Journal No. | 13 |
| Page no. | 11A13D: 1-13 |
| Keyword | Sentiment analysis, Machine learning, SVM linear grid, Twitter dataset, SVM model, SVM radial grid, Kappa value, SVM classifier, Negative sentiment, Neutral sentiment, Positive sentiment. |
| URL Website | http://TuEngr.com/Vol11_13.html |
| Website title | ITJEMAST V11(13) 2020 @ TuEngr.com |
| ISSN | 2228-9860 |
| Abstract | Sentiment analysis uses 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 a support vector machine (SVM) classifier with various parameters. The content of the 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 individuals. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve 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. |
