A Model for Customer Opinion Mining and Sentiment Classification using a Mixture of Experts Machine Learning Model

Abstract

This paper presents customer opinion mining technique based on a Mixture of Experts (MoE) machine learning model. The approach allows a corpus from open source data repositories to be classified into positive, negative and neutral sentiments as the case may be and in a predictive manner. The results of simulations showed that the proposed MoE approach can effectively be used as a core tool in opinion mining and also serve in decision making by appropriate categorizations. In particular, it was found that the use of higher epoch sizes greatly enhances the performance of the MoE by reducing perplexity and error cost margins to appreciable levels. Thus, the MoE presents a promising candidate for customer opinion mining particularly in business product development environments.

Keywords

• Classification
• Machine Learning
• opinion
• Sentiments

References

1. Banister, C. M., & Meriac, J. P. (2015). Political skill and work attitudes: A comparison of multiple social effectiveness constructs. The Journal of Psychology, 149(8), 775-795.
2. Ezenkwu, C. P., Ozuomba, S., & Kalu, C. (2015). Application of K-Means algorithm for efficient customer : a strategy for targeted customer services. International Journal of Advanced Research in Artificial Intelligence (IJARAI), 4(10), 40-44.
3. Kotzias, D., Denil, M., De Freitas, N., & Smyth, P. (2015, August). From group to individual labels using deep features. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 597-606).
4. Liu, B. (2012). Sentiment analysis: A fascinating problem. In Sentiment Analysis and Opinion Mining (pp. 1-8). Cham: Springer International Publishing.
5. Liu, B. (2020). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press.
6. Liu, B. (2022). Sentiment analysis and opinion mining. Springer Nature.
7. Ma, D., Li, S., Zhang, X., & Wang, H. (2017). Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893.
8. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070.

9. Shelke, N. M., Deshpande, S., & Thakre, V. (2012). Survey of techniques for opinion mining. International Journal of Computer Applications, 57(13).
10. Socher, R., Pennington, J., Huang, E. H., Ng, A. Y., & Manning, C. D. (2011, July). Semi-supervised recursive autoencoders for predicting sentiment distributions. In Proceedings of the 2011 conference on empirical methods in natural language processing (pp. 151-161).
11. Wang, G., Sun, J., Ma, J., Xu, K., & Gu, J. (2014). Sentiment classification: The contribution of ensemble learning. Decision support systems, 57, 77-93.
12. Wang, L., Peng, J., Zheng, C., & Zhao, T. (2024). A cross-modal hierarchical fusion multimodal sentiment analysis method based on multi-task learning. Information Processing & Management, 61(3), 103675.
13. Wang, W. (2024). Investor sentiment and stock market returns a story of night and day. The European Journal of Finance, 1-33.
14. Wang, Y., Huang, M., Zhu, X., & Zhao, L. (2016, November). Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the 2016 conference on empirical methods in natural language processing (pp. 606-615).
15. Williams, L., Bannister, C., Arribas-Ayllon, M., Preece, A., & Spasić, I. (2015). The role of idioms in sentiment analysis. Expert Systems with Applications, 42(21), 7375-7385.
16. Xia, R., Zong, C., & Li, S. (2011). Ensemble of feature sets and classification algorithms for sentiment classification. Information sciences, 181(6), 1138-1152.