An Improved Sentiment Analysis Algorithm Based on Appraisal Theory and Fuzzy Logic
محورهای موضوعی : Natural Language Processing
Azadeh Roustakiani
1
(Alzahra University)
Neda Abdolvand
2
(Alzahra University)
Saeideh Rajaei Harandi
3
(Social Sciences and Economics)
کلید واژه: Appraisal Theory, , Fuzzy Logic, , Sentiment Analysis, , Opinion Mining,
چکیده مقاله :
Millions of comments and opinions are posted daily on websites such as Twitter or Facebook. Users share their opinions on various topics. People need to know the opinions of other people in order to purchase consciously. Businesses also need customers’ opinions and big data analysis to continue serving customer-friendly services, manage customer complaints and suggestions, increase financial benefits, evaluate products, as well as for marketing and business development. With the development of social media, the importance of sentiment analysis has increased, and sentiment analysis has become a very popular topic among computer scientists and researchers, because it has many usages in market and customer feedback analysis. Most sentiment analysis methods suffice to split comments into three negative, positive and neutral categories. But Appraisal Theory considers other characteristics of opinion such as attitude, graduation and orientation which results in more precise analysis. Therefore, this research has proposed an algorithm that increases the accuracy of the sentiment analysis algorithms by combining appraisal theory and fuzzy logic. This algorithm was tested on Stanford data (25,000 comments on the film) and compared with a reliable dictionary. Finally, the algorithm reached the accuracy of 95%. The results of this research can help to manage customer complaints and suggestions, marketing and business development, and product testing.
Millions of comments and opinions are posted daily on websites such as Twitter or Facebook. Users share their opinions on various topics. People need to know the opinions of other people in order to purchase consciously. Businesses also need customers’ opinions and big data analysis to continue serving customer-friendly services, manage customer complaints and suggestions, increase financial benefits, evaluate products, as well as for marketing and business development. With the development of social media, the importance of sentiment analysis has increased, and sentiment analysis has become a very popular topic among computer scientists and researchers, because it has many usages in market and customer feedback analysis. Most sentiment analysis methods suffice to split comments into three negative, positive and neutral categories. But Appraisal Theory considers other characteristics of opinion such as attitude, graduation and orientation which results in more precise analysis. Therefore, this research has proposed an algorithm that increases the accuracy of the sentiment analysis algorithms by combining appraisal theory and fuzzy logic. This algorithm was tested on Stanford data (25,000 comments on the film) and compared with a reliable dictionary. Finally, the algorithm reached the accuracy of 95%. The results of this research can help to manage customer complaints and suggestions, marketing and business development, and product testing.
[1] J. Fletcher and J. Patrick, “Evaluating the Utility of Appraisal Hierarchies as a Method for Sentiment Classification”. Proceedings of the Australasian Language Technology Workshop 2005, (December 2005), pp. 134–142.
[2] B. Pang, and L. Lee, “Opinion Mining and Sentiment Analysis”. Foundations and Trends in Information Retrieval, Vol. 1, No. 2, 2006, pp. 91–231.
[3] P. Korenek, and M. Šimko, “Sentiment analysis on microblog utilizing appraisal theory.” World Wide Web, Vol. 17, No. 4, 2014, 847–867.
[4] M. A. Kanade, M. A. Deshmukh, M. S. Surwase, M.A. Kulkarni, and A. Zore, “Implementation on Intelligent Sentiment Review Analysis with Short Form Words and False Negative Comment Consideration”. International Journal of Research in Engineering, Technology and Science, Vol. 3, No. 4, April 2018.
[5] J. Brooke, “A Semantic Approach to Automated Text Sentiment Analysis, 118.” Retrieved from http://www.sfu.ca/~mtaboada/docs/Julian_Brooke_MA.2009.
[6] L. Zhang, and B. Liu, “Sentiment analysis and opinion mining. In Encyclopedia of Machine Learning and Data Mining” (pp. 1152-1161). Springer, Boston, MA. 2017.
[7] A. Pak, and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining”. In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 2010, pp. 1320–1326.
[8] B. Pang, and L. Lee, “A Sentimental Education: Sentiment Analysis using Subjectivity Summation based on Minimum Cuts”. ACL ’04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, 2004, p. 271.
[9] T. Wilson, J. Wiebe, and P. Hoffman, “Recognizing contextual polarity in phrase level sentiment analysis”. Acl, Vol. 7, No. 5, 2005, pp. 12–21.
[10] C. Whitelaw, C. Whitelaw, N. Garg, N. Garg, S. Argamon and S. Argamon, S. “Using appraisal groups for sentiment analysis”. Proceedings of the 14th ACM International Conference on Information and Knowledge Management - CIKM ’05, 2005, p. 625.
[11] C. S.-G. Khoo, A. Nourbakhsh, A., and N. Jin-Cheon, N. “Sentiment analysis of online news text: a case study of appraisal theory”. Online Information Review, Vol. 36, No. 6, 2012, pp. 858–878.
[12] K. Bloom, K. Sentiment analysis based on appraisal theory and functional local grammars. Illinois Institute of Technology. ProQuest Dissertations Publishing, 2011. 3504518.
[13] M. Dragoni, A. G. Tettamanzi, and C. da Costa Pereira, (2014, May). “A fuzzy system for concept-level sentiment analysis.” In Semantic web evaluation challenge (pp. 21-27). Springer, Cham.
[14] B. Keith, E. Fuentes, and C. Meneses, “A Hybrid Approach for Sentiment Analysis Applied to Paper”. In Proceedings of ACM SIGKDD Conference, Halifax, Nova Scotia, Canada, August 2017 (KDD’17), 2017, P. 10 .
[15] S. Moghaddam, “Beyond sentiment analysis: mining defects and improvements from customer feedback.” In European Conference on Information Retrieval (pp. 400-410). Springer, Cham. 2015, March.
[16] H. Li, J. Ding, D. Nie, and L. Tang, “Accurate Recommendation Based on Opinion Mining”. Advances in Intelligent Systems and Computing, Vol. 329, 2015, pp. 325–333.
[17] K. Denecke and Y. Deng, “Sentiment analysis in medical settings: New opportunities and challenges”. Artificial Intelligence in Medicine, Vol 64, No. 1, 2015, pp. 17–27.
[18] A. Andreevskaia, and S. Bergler, “Mining WordNet for fuzzy sentiment: Sentiment tag extraction from WordNet glosses”. Proceedings of EACL, 6, 2006, pp. 209–216.
[19] T. Rahman, “Sentiment Analysis by Using Fuzzy Logic”, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No. 1, 2014, pp. 33–48.
[20] D. K. Tayal, and S. K. Yadav, “Word level sentiment analysis using fuzzy sets”. Analysis, Vol. 54, 2015, p. 59.
[21] B. V. Krishna, A. K. Pandey, and A. S. Kumar, “Feature Based Opinion Mining and Sentiment Analysis Using Fuzzy Logic”. In Cognitive Science and Artificial Intelligence (pp. 79-89). Springer, Singapore. 2018.
[22] O. Appel, F. Chiclana, J. Carter, and H. Fujita, “A hybrid approach to the sentiment analysis problem at the sentence level”. Knowledge-Based Systems, Vol. 108, 2016, pp. 110-124.
[23] P. Haseena Rahmath. “Fuzzy based Sentiment Analysis of Online Product Reviews using Machine Learning Techniques.” International Journal of Computer Applications (0975 – 8887). Vol. 99, No. 17, 2014, pp. 9-16.
[24] A. Alamsyah, W. Rahmah, and H. Irawan, “Sentiment analysis based on appraisal theory for marketing intelligence in Indonesia?” Mobile phone market. Journal of Theoretical and Applied Information Technology, Vol. 82, No. 2, 2015, pp. 335–340.
[25] A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning Word Vectors for Sentiment Analysis”. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011. pp. 142–150.
