The bravo: A framework of building reputation analytics from voice online

Abstract

This paper provides a framework to efficiently discover production performance and its application plan by analyzing massive amounts of comments from online review data, especially in the field of hospitality. In order to achieve the goal, two stages of text analytics of sentiment analysis and structural topic model estimating are integrated to classify sentimental polarity of each reviews and elicit hidden dimensions of products or services. Based on these dimensions and polarities, this paper verifies key attributes which impact customer satisfaction by adapting logistic regression. This study extends prior research limitation which focused on discovering the product defect by (1) strength detection, (2) time series analysis, and (3) explanation of the relationship between crucial factors and polarity of the review as a proxy of customer satisfaction. By integrating text analytics from computational linguistic and a traditional statistical method, this paper is expected to contribute on both academical and practical implications.