Personalized Information Visualization of Online Product Reviews

Author(s):  
Jooyoung Kim ◽  
Dongsoo Kim
2018 ◽  
Vol 51 (1-3) ◽  
pp. 25-49
Author(s):  
Ravi KUMAR ◽  
Teja SANTOSH DANDIBHOTLA ◽  
Vishnu VARDHAN BULUSU

2018 ◽  
Vol 13 (4) ◽  
pp. 192 ◽  
Author(s):  
Li Yang

It is widely proved that positive online word-of-mouth (WOM) can boost sales and negative online WOM harm sales. Then will more positivity or negativity of messages in online product reviews text have greater impact on product sales? This research attempts to tackle this ignored research question. The answer is counter-intuitive: it depends on how positive or negative they are! The results of a two-way fixed-effects panel data analysis based on the data about tablet market in Amazon and a novel sentiment analysis technique demonstrate that the most and least polarized online product reviews actually have no effect on sales and only moderate positive / negative reviews can affect sales. Such effects can be explained by the optimal arousal theory and attribution theory. Inspired by the findings, three strategies for user-generated content (UGC) management are proposed.


2021 ◽  
Author(s):  
Joanne DiNova

This paper examines the use of language in user generated online product reviews on the website Yelp.ca. Using both Relevance Theory and the Co-operative Principle this study identifies nine linguistic devices to analyze within restaurant reviews on this website. Yelp.ca administrators identify some reviewers as “Elite Reviewers.” This study contrasted twenty-five Elite reviews with twenty-five Non-Elite reviews in order to determine which linguistic devices were more prevalent within Elite reviews. The findings illustrate that there are concrete differences between these two types of reviews. Assuming that Elite Reviews are in fact more persuasive, these findings suggest that there may be concrete attributes of a review that make it more persuasive in an online, user generated context.


2021 ◽  
Author(s):  
Joanne DiNova

This paper examines the use of language in user generated online product reviews on the website Yelp.ca. Using both Relevance Theory and the Co-operative Principle this study identifies nine linguistic devices to analyze within restaurant reviews on this website. Yelp.ca administrators identify some reviewers as “Elite Reviewers.” This study contrasted twenty-five Elite reviews with twenty-five Non-Elite reviews in order to determine which linguistic devices were more prevalent within Elite reviews. The findings illustrate that there are concrete differences between these two types of reviews. Assuming that Elite Reviews are in fact more persuasive, these findings suggest that there may be concrete attributes of a review that make it more persuasive in an online, user generated context.


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