Collective Extraction for Opinion Targets and Opinion Words from Online Reviews

2016 ◽  
Author(s):  
Xiangxiang Jiang ◽  
Yuming Lin ◽  
You Li ◽  
Jingwei Zhang
Keyword(s):  
Online Reviews

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

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.

scholarly journals The Beholder’s Eyes: Audience Reactions to Organizational Self-claims of Authenticity

2018 ◽  
Vol 4 ◽  
pp. 237802311879303 ◽  
Author(s):  
David W. Lehman ◽  
Balázs Kovács ◽  
Glenn R. Carroll
Keyword(s):  
Major Part ◽  
Online Reviews ◽  
Emotional Reactions ◽  
Category Type ◽  
Moral Claims ◽  
Made In

Organizations normally benefit from being perceived as authentic. Yet an ongoing puzzle persists about self-claims of authenticity: although the weight of findings suggests that individuals will devalue organizations touting themselves as authentic, some findings suggest that such self-claims may be rewarded. The authors suggest that this puzzle can be answered, at least partly, by considering two fundamental but different meanings of authenticity. The authors posit that individuals will react negatively when claims concern being true to a category (“type authenticity”), whereas they will react positively when claims concern being true to the organization’s values (“moral authenticity”). A major part of this reasoning involves the emotional reactions evoked by moral claims. In study 1, authenticity claims made in the texts of 1,393 restaurant menus and corresponding ratings of 449,919 online reviews from 2009 to 2016 were analyzed. Study 2 involved an experiment to examine reactions to the two kinds of claims separately. The findings generally support the argument and apparently help explain audience reactions to organizational self-claims about authenticity.

The effect of emotions, eWOM quality and online review sequence on consumer intention to follow advice obtained from digital services

2020 ◽  
Vol 31 (3) ◽  
pp. 465-487 ◽  
Author(s):  
Carla Ruiz-Mafe ◽  
Enrique Bigné-Alcañiz ◽  
Rafael Currás-Pérez
Keyword(s):  
Structural Model ◽  
Online Reviews ◽  
Moderating Effect ◽  
Online Information ◽  
Bias Effect ◽  
Content Type ◽  
Digital Services ◽  
Information Cues ◽  
Cognitive Information ◽  
The Impact

PurposeThis paper analyses the interrelationships between emotions, the cognitive information cues of online reviews and intention to follow the advice obtained from digital platforms, paying special attention to the moderating effect of the sequencing of review valence.Design/methodology/approachThe data were collected from 830 Spanish Tripadvisor users. In a two-step approach, a measurement model was estimated and a structural model analysed to test the proposed hypotheses. SmartPLS 3.0 software was used. The moderating effect of sequencing of reviews is tested.FindingsThe data analysis showed a bias effect of review sequence on the impact of online information cues and emotions on intention to follow advice obtained from Tripadvisor. When the online reviews of a restaurant begin with positive commentaries, their perceived persuasiveness is a stronger driver of the pleasure and arousal elicited by online reviews than when they begin with negative reviews. On the other hand, the perceived helpfulness of online reviews only triggers arousal when the user reads negative, followed by positive, comments. The impact of pleasure on intention to follow the advice provided in an online travel community is higher with positive-negative than with negative-positive sequences.Originality/valueWhile researchers have demonstrated the benefits of customer reviews on company sales, a largely uninvestigated issue is the interplay between emotions and cognitive information cues in the processing of online reviews. This is one of the first studies to examine the moderating effect of conflicting reviews on the impact of emotions and cognitive information cues on consumer intention to follow the advice obtained from digital services.

scholarly journals Influence of MOOC learners discussion forum social interactions on online reviews of MOOC

2021 ◽  
Author(s):  
Bing Wu
Keyword(s):  
Social Interactions ◽  
Regression Models ◽  
Online Courses ◽  
Online Reviews ◽  
Discussion Forum ◽  
Massive Open Online Courses ◽  
Online Review ◽  
Discussion Forums ◽  
Processing Data ◽  
Learning Progress

AbstractAlthough some studies have explored massive open online courses (MOOCs) discussion forums and MOOC online reviews separately, studies of both aspects are insufficient. Based on the theory of self-determination, this paper proposes research hypotheses that MOOC learning progress has a direct impact on MOOC online reviews and an indirect influence on MOOC online reviews through social interactions in discussion forums, as well. Coursera the largest MOOC platform, is selected as the empirical research object, and data from learners who participated in the MOOC discussion forum and provided MOOC online reviews from August 2016 to December 2019 are obtained from the most popular course, “Machine Learning”. After processing, data from 4376 learners are obtained. Then, according to research hypotheses, multi regression models are constructed accordingly. The results show that the length of MOOC online review text is affected by the MOOC learning progress, the number of discussion forum posts, the number of follow, the online review sentiment and MOOC rating. This study highlights the main factors that affect MOOC online reviews. As a result, some suggestions are put forward for the construction of MOOC.

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