Market Segmentation of Online Reviews: A Network Analysis Approach

With the popularity of online reviews, brand managers have opportunities to segment their markets according to the reviews of their products or services by customers. Nonetheless, it has been suggested that traditional market segmentation methods are ineffective at analyzing online review data due to the complex features and large amount of this type of data; specifically, traditional methods fail to take into account the networked nature of interactive relationships among reviewers and brands across online review websites. Accordingly, this study proposes a network analysis approach for the market segmentation of online reviews. Collecting samples from Yelp via web scraping, this study demonstrates how network analysis techniques can be utilized to segment online reviewers through a four-step process. The results reveal the core and peripheral market segments, as well as the bridge segment in the core. The study contributes to offering marketing researchers and managers a new network structure analysis approach for the market segmentation of online reviews.

“Better Not Let Me Know”: The Mediating Role of Regret on the Relation Between Social Comparison Discrepancy in Online Hotel Review and Revisit Intention

Online review is powerful in influencing tourists’ travel decision. However, understanding of how online reviews affect tourist emotion and decision at the post-trip stage is limited. The present study examines whether encountering travel experiences shared on social media by other users with disparity in the cost of same accommodation after a trip will cause regret and alter the intention to revisit from a retrospective point of view. Drawing from the experimental study, the result showed that regret mediates the negative effect of comparison discrepancy (in the case of differences in the paid room rate) on intention to stay in the hotel again. The current study of the effects of social comparison on revisit intention adds to the literature and establishes the groundwork for future scholarly work on post-trip online review management. Meaningful implications and strategies are recommended to online review platform and hotel marketing management.

Study of E-Commerce Customer Review Capturing Process in the Indian Context

Online customer reviews provided by customers on e-commerce sites who had bought the products proved to be a key parameter. New and potential customers at the pre-purchase stage to vet the merits and demerits before buying new products listed on e-commerce sites referred to online customer reviews. However, there have been very few studies that focused on online customer review capturing process. Thus, this research work focused on the review capturing process of e-commerce websites from a customer's point of view to understand the online customer review process. A qualitative exploratory research was carried out. An open-ended semi-structured questionnaire was used to understand customer's stand on the e-commerce review capturing process. In-depth interviews were collected from customers. The data was analyzed thematic content. The study findings indicated what motivated customers to write online reviews, what inhibited them from writing reviews and what were their suggestions for the managers of e-commerce organizations towards designing better online review capturing.

Sentiment Analysis Using Hybrid Approach

Most important part of information gathering is to focus on how people think. There are so many opinion resources such as online review sites and personal blogs are available. In this paper we focused on the Twitter. Twitter allow user to express his opinion on variety of entities. We performed sentiment analysis on tweets using Text Mining methods such as Lexicon and Machine Learning Approach. We performed Sentiment Analysis in two steps, first by searching the polarity words from the pool of words that are already predefined in lexicon dictionary and in Second step training the machine learning algorithm using polarities given in the first step. Keywords: Sentiment analysis, Social Media, Twitter, Lexicon Dictionary, Machine Learning Classifiers, SVM.

The impact of online reviews on destination trust and travel intention: The moderating role of online review trustworthiness

This study explores the impact of the valence (positive/negative) and emotional intensity (strong/weak) of online reviews on potential Chinese visitors’ travel intentions and trust of a destination. An experimental design was used to test the hypotheses. Findings suggest that online review valence and emotional intensity affect travel intentions and that destination trust can partially mediate this relationship. Changes in destination trust and travel intention due to positive/negative review emotional intensity changes are not equivalent. Furthermore, online review trustworthiness moderates the valence and destination trust and travel intention relationships, but not the effect of review emotional intensity on the same outcomes.