Username   Password       Forgot your password?  Forgot your username? 


An Improved Text Sentiment Analysis Algorithm based on TF-Gini

Volume 14, Number 9, September 2018, pp. 2008-2014
DOI: 10.23940/ijpe.18.09.p8.20082014

Songtao Shang, Yong Gan, and Huaiguang Wu

School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

(Submitted on May 24, 2018; Revised on July 11, 2018; Accepted on August 11, 2018)


With the development of social media, more and more people prefer to express their opinions on the Internet. Therefore, developing a way to mine people’s emotional attitudes has become an important area of research. Text sentiment analysis is a method to mine people’s emotional attitudes through texts and an effective tool to grasp Internet users’ emotional tendencies. Naïve Bayes is a reliable text classification algorithm that has been approved by many researchers. Feature weighting is the most important problem for Naïve Bayes. Hence, this paper proposes an improved feature weighting algorithm, entitled TF-Gini, to enhance the performance of Naïve Bayes. The experimental results demonstrate the effectiveness of the improved algorithm.


References: 17

              1. J. K. Rout, K. R. Choo, A. K. Dash, S. Bakshi, S. K. Jena, and K. L. Williams, “A Model for Sentiment and Emotion Analysis of Unstructured Social Media Text,” Electronic Commerce Research, No. 2, pp.1-19, 2018
              2. B. Liu, “Sentiment Analysis and Opinion Mining,” Synthesis Lectures on Human Language Technologies, pp. 1-167, 2012
              3. Y. Zhao, B. Qin, and T. Liu, “Sentiment Analysis,” Journal of Software, Vol. 21, No. 8, pp. 1834-1848, 2010
              4. S. Bolouki, R. P. Malhame, M. Siami, and N. Motee, “Eminence Grise Coalitions: On the Shaping of Public Opinion”, IEEE Transactions on Control of Network System, Vol. 4, No. 2, pp. 133-145, 2017
              5. S. M. Kim and E. Hovy, “Determining the Sentiment of Opinions,” in Proceedings of the 20th Conference on Computational Linguistics, pp. 1367-1373, 2004
              6. J. Singh, G. singh, and R. Singh, “A Review of Sentiment Analysis Techniques for Opinionated Web Text,” Csi Transactions in ICT, Vol. 4, No. 2-4, pp. 241-247, 2016
              7. S. Rani and P. Kumar, “A Sentiment Analysis System to Improve Teaching and Learning,” Computer, Vol. 50, No. 5, pp. 36-43, 2017
              8. R. Pandarachalil, S. Sendhilkumar, and G. S. Mahalakshmi, “Twitter Sentiment Analysis for Large-Scale Data: An Unsuperivsed Approach,” Cognitive Computation, Vol. 7, No. 2, pp. 254-262, 2015
              9. W. Fu, W. Liu, Y. Xu, and L. Cui, “Combine HowNet Lexicon to Train Phrase Recursive Autoencoder for Senteence-level Sentiment Analysis,” Neurocomputing, pp. 18-27, 2017
              10. G. M. Emelyanov, D. V. Mikhailov, and A. P. Kozlov, “The TF-IDF Measure and Analysis of Links Between Words Within N-grams in the Formation of Knowledge Units for Open Tests,” Pattern Recognition and Image Analysis, Vol. 27, No. 4, pp. 825-831, 2017
              11. B. Tang, S. Kay, and H. He, “Toward Optimal Feature Selection in Naive Bayes for Text Categorization,” IEEE Transactions on Knowledge and Data Engineering, Vol. 28, No. 9, pp. 2508-2521, 2016
              12. C. Slamet, A. R. Atmadja, D. S. Maylawati, R. S. Lestari, W. Darmalakasana, and M. A. Ramdhani, “Atuomated Text Summarization for Indonesian Article Using Vector Space Model,” in Proceedings of IOP Conference Series: Materials Science and Engineering, Vol. 288, No. 1, pp. 12-37, 2018
              13. F. Zeng, Y. Li, and M. D. Levine, “Contextual Bag-of-Words for Robust Visual Tracking,” IEEE Transactions on Image Processing, Vol. 27, No. 3, pp. 1433-1447, 2018
              14. Z. Bao, J. Lu, T. K. Ling, and B. Chen, “Towards an Effective XML Keyword Search,” IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 8, pp. 1077-1092, 2010
              15. L. E. Raileanu and K. Stoffel, “Theoretical Comparison Between the Gini Index and Information Gain Criteria,” Annals of Mathematics and Artificial Intelligence, Vol. 41, No. 1, pp. 77-93, 2004
              16. M. Ohasaki, P. Wang, K. Matsuda, S. Katagiri, H. Watanabe, and A. Ralescu, “Confusion-Matrix-based Kernel Logistic Regression for Imbalanced Data Classification,” IEEE Transactions on Knowledge and Data Engineering, Vol. 29, No. 9, pp. 1806-1819, 2017
              17. C. J. Rijsbergen, “Information Retrieval,” London: Butterworths, 1979


                          Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

                          This site uses encryption for transmitting your passwords.