References
- 1. Akila, R., Revathi, S., Shreedevi, G. (2020), “Opinion Mining on Food Services using Topic Modeling and Machine Learning Algorithms”, in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1071-1076.10.1109/ICACCS48705.2020.9074428
- 2. Akter, S., Aziz, M. T. (2016), “Sentiment analysis on facebook group using lexicon based approach”, in 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1-4.10.1109/CEEICT.2016.7873080
- 3. Balan, S., Rege, J. (2017), “Mining for social media: Usage patterns of small businesses”, Business Systems Research: The Journal of Society for Advancing Innovation and Research in Economy, Vol. 8 No. 1, pp. 43-50.10.1515/bsrj-2017-0004
- 4. Burges, C. J. (1998), “A tutorial on support vector machines for pattern recognition”, Data mining and knowledge discovery, Vol. 2 No. 2, pp. 121-167.10.1023/A:1009715923555
- 5. Dunđer, I., Horvat, M., Lugović, S. (2016), “Word occurrences and emotions in social media: Case study on a Twitter corpus”, in Biljanović, P. (Ed.), Proceedings of the 39th International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2016, Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO, Rijeka, pp. 1557-1560.10.1109/MIPRO.2016.7522337
- 6. Joachims, T. (2002), Learning to classify text using support vector machines, Springer Science & Business Media.10.1007/978-1-4615-0907-3
- 7. Kadriu, A., Abazi, L., Abazi, H. (2019), “Albanian Text Classification: Bag of Words Model and Word Analogies”, Business Systems Research: The Journal of the Society for Advancing Innovation and Research in Economy, Vol. 10 No. 1, pp. 74-87.10.2478/bsrj-2019-0006
- 8. Khairnar, J., Kinikar, M. (2013), “Machine learning algorithms for opinion mining and sentiment classification”, International Journal of Scientific and Research Publications, Vol. 3 No. 6, pp. 1-6.
- 9. Krstić, Ž., Seljan, S., Zoroja, J. (2019), “Visualization of Big Data Text Analytics in Financial Industry: A Case Study of Topic Extraction for Italian Banks”, Entrenova, Vol. 5 No. 1, pp. 67-75.10.2139/ssrn.3490108
- 10. Le, H. S., Trieu, C., Ho, T., Lee, J. H., Lee, H. K. (2017), “Applying Artificial Neural Network for Sentiment Analytics of Social Media Text Data in fastfood industry”, Internet e-commerce research, Vol. 17 No. 5, pp. 113-123.
- 11. Li, Z., Fan, Y., Jiang, B., Lei, T., Liu, W. (2019), “A survey on sentiment analysis and opinion mining for social multimedia”, Multimedia Tools and Applications, Vol. 78 No. 6, pp. 6939-6967.10.1007/s11042-018-6445-z
- 12. Liu, B. (2012), “Sentiment analysis and opinion mining”, Synthesis lectures on human language technologies, Vol. 5 No. 1, pp. 1-167.10.2200/S00416ED1V01Y201204HLT016
- 13. Liu, B. (2017), “Many facets of sentiment analysis”, in A practical guide to sentiment analysis, pp. 11-39.10.1007/978-3-319-55394-8_2
- 14. Lugović, S., Dunđer, I., Horvat, M. (2016), “Techniques and applications of emotion recognition in speech”, in Proceedings of MIPRO, pp. 1278-1283.10.1109/MIPRO.2016.7522336
- 15. Maks, I., Vossen, P. (2012), “A lexicon model for deep sentiment analysis and opinion mining applications”, Decision Support Systems, Vol. 53 No. 4, pp. 680-688.10.1016/j.dss.2012.05.025
- 16. Mudambi, S. M., Schuff, D. (2010), “What makes a helpful review? A study of customer reviews on Amazon.com”, MIS Quarterly, Vol. 34 No. 1, pp. 185-200.10.2307/20721420
- 17. Nagpal, M., Kansal, K., Chopra, A., Gautam, N., Jain, V. K. (2020), “Effective Approach for Sentiment Analysis of Food Delivery Apps”, in Soft Computing: Theories and Applications, pp. 527-536.10.1007/978-981-15-0751-9_49
- 18. Nguyen, H., Ho, T. (2020), “Topic modeling for analyzing online reviews in hotel sector”, Science & Technology Development Journal - Economics - Law and Management, Vol. 4 No. 4, pp. 1081-1092.10.32508/stdjelm.v4i4.692
- 19. Ohana, B., Tierney, B. (2009), “Sentiment classification of reviews using SentiWordNet”, in the 9th IT&T conference, pp. 18-30.
- 20. Pang, B., Lee, L. (2008), “Opinion mining and sentiment analysis”, Foundations Trends Information Retrieval, Vol. 2 No. 1-2, pp. 1-135.10.1561/9781601981516
- 21. Patel, R., Sornalakshmi, K. (2020), “Sentiment Analysis of Food Reviews Using User Rating Score”, in Artificial Intelligence Techniques for Advanced Computing Applications, pp. 415-431.10.1007/978-981-15-5329-5_39
- 22. Pejić Bach, M., Krstić, Ž., Seljan, S. (2019), “Big data text mining in the financial sector”, in Metawa, N., Elhoseny, M., Hassanien, A. E., Hassan, M. K. (Eds.), Expert Systems in Finance: Smart Financial Applications in Big Data Environments, Routledge, pp. 80-96.10.4324/9780429024061-6
- 23. Sun, S., Luo, C., Chen, J. (2017), “A review of natural language processing techniques for opinion mining systems”, Information fusion, Vol. 36, pp. 10-25.10.1016/j.inffus.2016.10.004
- 24. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M. (2011), “Lexicon-based methods for sentiment analysis”, Computational linguistics, Vol. 37 No. 2, pp. 267-307.10.1162/COLI_a_00049
- 25. Vu, L., Le, T. (2017), “A lexicon-based method for Sentiment Analysis using social network data”, in Proceedings of the International Conference on Information and Knowledge Engineering (IKE), pp. 10-16.
- 26. Vu, T. T., Pham, H. T., Luu, C. T., Ha, Q. T. (2011), “A feature-based opinion mining model on product reviews in Vietnamese”, in Semantic Methods for Knowledge Management and Communication, pp. 23-33.10.1007/978-3-642-23418-7_3
- 27. Yadav, S. K. (2015), “Sentiment analysis and classification: a survey”, International Journal of Advance Research in Computer Science and Management Studies, Vol. 3 No. 3, pp. 113-121.
- 28. Yang, K., Cai, Y., Huang, D., Li, J., Zhou, Z., Lei, X. (2017), “An effective hybrid model for opinion mining and sentiment analysis”, in 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 465-466.10.1109/BIGCOMP.2017.7881759