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

Year 2024, , 51 - 61, 06.06.2024

Vincent Ike Anıreh , Emmanuel Ndidi Osegi , Aa Silas

https://doi.org/10.53070/bbd.1409094

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

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