Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

Abstract

Online consumer reviews in e-commerce are one technique to gather consumer opinion and sentiment about a company's products and services. However, manual analysis is impractical due to natural language text's enormous volume and complexity. Text mining and sentiment analysis methods based on machine learning provide an opportunity to analyze data for marketing objectives by increasing sales, positive electronic word-of-mouth (e-WOM), and meeting consumer demands and wants through the enhancement of market offerings. Despite the numerous benefits of analyzing e-commerce reviews to assist a company's marketing strategy, very little research has focused on sentiment and acceptance for Malaysia’s local agriculture products due to mixed language (English-Malay language) processing challenges. This concept paper highlights the use of text mining techniques to extract valuable insights from e-commerce comments related to Malaysian local agriculture products. By leveraging text mining, the study aims to better understand consumer sentiments, preferences, and feedback regarding local products, thereby facilitating improved market analysis and decision-making processes.