Pages 56–90

Authors

Kamonwit Makkaphan, Prompong Sungunnasil, Waranya Mahanan, and Sumalee Sangamuang

Abstract

Online customer reviews represent a valuable source of information for businesses seeking to understand consumer perceptions and preferences. This paper introduces a framework for competitive positioning analysis by leveraging these online reviews and sentiment analysis. The framework employs Natural Language Processing (NLP) techniques in three phases: 1) identifying key themes and topics from reviews using Latent Dirichlet Allocation (LDA); 2) extracting product features through zero-shot text classification; and 3) visualizing competitive positioning via Net Promoter Score (NPS) and sentiment analysis plots. A case study on Amazon’s laptop market revealed a moderate correlation (58.8%) between NPS and sentiment analysis, suggesting potential limitations in feature classification accuracy. While the study demonstrates the value of NLP for analyzing online reviews, it also emphasizes the need for improved feature recognition methods and more robust datasets to enhance the precision of competitive positioning analysis.