Genre Change in the Online Context: Responding to Negative Online Reviews and Redefining an Effective Genre Construct on Amazon.Com

2021 ◽  
pp. 105065192110011
Author(s):  
Junhua Wang
Keyword(s):  
Online Reviews ◽  
Genre Analysis ◽  
Genre Change

This study examines 50 business responses to negative reviews on Amazon.com in order to identify common genre moves for responding to negative online reviews. To complement the genre analysis and assess the effectiveness of these common genre moves, the author conducted a survey seeking consumers’ feedback on three typical business responses to negative online reviews. This investigation not only provides feedback on how businesses can publicly respond to negative online reviews but also presents an empirical case on how we can balance genre stability and variation and go beyond just teaching typified genre features in our genre pedagogy.

scholarly journals A GENRE-BASED TEXTUAL ANALYSIS OF EXPERT ONLINE REVIEWS OF ELECTRIC GUITAR FUZZ PEDALS

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Che Moya
Keyword(s):  
Data Mining ◽  
Online Reviews ◽  
Genre Analysis ◽  
Discourse Community ◽  
Consumer Spending ◽  
Linguistic Features ◽  
Electric Guitar ◽  
Online Data ◽  
Real World Applications ◽  
Insight Into

This study uses genre analysis to analyze contemporary online text.  Through an analysis of the linguistic features of text from a highly specialized discourse community, perceived expert online reviews of electric guitar fuzz pedals, findings from this study provide insight into the relevance of online reviews. Although there is a layman quality to the actual production of the textual online reviews, genre analysis reveals hybridized genres, authorial power within occluded genre chains, subconscious marketing techniques, and manipulation of perceived expert online reviews. Findings from this study indicate that online reviews have complex occluded genre chains, which are not readily obvious when the reviews are read only for entertainment purposes.  This research has real world applications as relationships between textual generic qualities of online reviews and consumer spending habits are now easily acquired from online data-mining techniques to assist in targeting online consumers’ habits and to increase sales. Online reviews are now what consumers use to assist in shopping. Control of reviews translates to control of consumers’ shopping habits.

ANALYSING SENTIMENTS OF ONLINE REVIEWS ON RESTAURANTS IN MALAYSIA USING PREDICTIVE TEXT ANALYTICS

2018 ◽  
Vol 2018 ◽  
pp. 200-200
Author(s):  
Kok Wei Khong ◽  
◽  
Fon Sim Ong ◽  
Babajide AbuBakr Muritala ◽  
Ken Kyid Yeoh
Keyword(s):  
Online Reviews ◽  
Text Analytics

Genre analysis today

1997 ◽  
Vol 75 (3) ◽  
pp. 629-652 ◽  
Author(s):  
Vijay K. Bhatia
Keyword(s):  
Genre Analysis

Management Responses to Online Reviews: Consumer Preferences of Action Frames and Language Style

2020 ◽  
Author(s):  
Agne Bendaraviciute ◽  
Philipp Wassler ◽  
Thi Hong Hai Nguyen ◽  
Simon Thomas
Keyword(s):  
Figurative Language ◽  
Consumer Preferences ◽  
Online Reviews ◽  
Theory And Practice ◽  
Discrete Choice Experiments ◽  
Hotel Management ◽  
Language Style ◽  
Multi Method Approach ◽  
Language Styles ◽  
Depth Interviews

This study was taken as the understanding of management responses remains scarcely understood in theory and practice, especially concerning consumer preferences. This study aims at examining consumer preferences of the action frames and language styles adopted in hotel management responses to online reviews. A multi-method approach, using discrete choice experiments followed by in-depth interviews, was employed. Findings show that past action frames in management responses are preferred by customers due to the certainty, trustworthiness and detailed information provided. Moreover, literal is favoured over figurative language style due to perceived professionalism and conciseness. The current study helps hotel management to further understand consumer preferences of management responses to online reviews, especially regarding action frames and language styles.

Inducing Consumer Online Reviews via Disclosure

2018 ◽  
Author(s):  
Xu Guan ◽  
Yulan Wang ◽  
Zelong Yi ◽  
Ying-Ju Chen
Keyword(s):  
Online Reviews

Herding, Learning and Incentives for Online Reviews

2020 ◽  
Author(s):  
Rajeev Kohli ◽  
Xiao Lei ◽  
Yeqing Zhou
Keyword(s):  
Online Reviews

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

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