scholarly journals What is there to Yelp about?

2021 ◽  
Author(s):  
Joanne DiNova
Keyword(s):  
Relevance Theory ◽  
Product Reviews ◽  
Online Product Reviews ◽  
Restaurant Reviews

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.

scholarly journals What is there to Yelp about?

2021 ◽  
Author(s):  
Joanne DiNova
Keyword(s):  
Relevance Theory ◽  
Product Reviews ◽  
Online Product Reviews ◽  
Restaurant Reviews

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.

Learned ontology guided opinions analysis of extracted aspects from online product reviews

2018 ◽  
Vol 51 (1-3) ◽  
pp. 25-49
Author(s):  
Ravi KUMAR ◽  
Teja SANTOSH DANDIBHOTLA ◽  
Vishnu VARDHAN BULUSU
Keyword(s):  
Product Reviews ◽  
Online Product Reviews

scholarly journals More Is Less: Only Moderate Polarized Online Product Reviews can Affect Sales

2018 ◽  
Vol 13 (4) ◽  
pp. 192 ◽  
Author(s):  
Li Yang
Keyword(s):  
Attribution Theory ◽  
Fixed Effects ◽  
Panel Data Analysis ◽  
Research Question ◽  
Product Reviews ◽  
Online Product Reviews ◽  
Arousal Theory ◽  
Online Word Of Mouth ◽  
Analysis Technique ◽  
Product Sales

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.

scholarly journals A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

2021 ◽  
Vol 22 (1) ◽  
pp. 53-66
Author(s):  
D. Anand Joseph Daniel ◽  
M. Janaki Meena
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
User Satisfaction ◽  
Performance Metrics ◽  
Computation Time ◽  
Feature Reduction ◽  
Training Data ◽  
Product Reviews ◽  
Online Product Reviews

Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.

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