Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in

Kaushik, Kapil; Mishra, Rajhans; Rana, Nripendra P.; Dwivedi, Yogesh K.

doi:10.1016/j.jretconser.2018.08.002

Cited by 80 publications

(76 citation statements)

References 60 publications

(75 reference statements)

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“…A handful of studies (Ahmad & Laroche, 2015;Kim & Gupta, 2012;Yin et al, 2014) have examined the impact of emotions expressed in review content on perceived helpfulness of online reviews. Overall star rating, rating inconsistency Baek et al, 2012;Hu & Chen, 2016;Mudambi & Schuff, 2010;Robinson et al, 2012;Yan et al, 2016;Yin et al, 2016 Content Length, proportion of negative words, images/photos, valence, objectivity/subjectivity, emotions, emotion intensity, detailed information, explained actions and reactions, review format, review type (attributed value and simple recommendation), review Ahmad & Laroche, 2015;Baek et al, 2012;Cheng & Ho, 2015;Felbermayr & Nanopoulos, 2016;Folse et al, 2016;Hussain, et al 2017;Jeong & Koo, 2015;Karimi & Wang, 2017;Kaushik et al, 2018;Kim & Gupta, 2012;Li & Zhan, 2011;Lockie et al, 2015;Moore, 2015;Mudambi & Schuff, 2010;Ngo- Ye & diagnosticity, technical information, argument diversity, expertise claim, persuasive words, presentation mode Sinha, 2014; Park & Lee, 2008;Peng et al, 2014;Purnawirawan et al, 2015;Quaschning et al, 2015;Robinson et al, 2012;Teng et al, 2014;Weathers et al, 2015;Willemsen et al, 2011;Wu, 2013;…”

Section: Literature Reviewmentioning

confidence: 99%

Perceived helpfulness of eWOM: Emotions, fairness and rationality

Ismagilova

Dwivedi

Slade

2020

Journal of Retailing and Consumer Services

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102

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Consumers use online reviews to help make informed purchase decisions. This paper extends existing research by examining how content of online reviews influences perceptions of helpfulness by demonstrating how different emotions can influence helpfulness of both product and service online reviews beyond a valence-based approach using cognitive appraisal theory and attribution theory. This research contributes to existing knowledge regarding the theory of information processing, attribution theory, and cognitive appraisal theory of emotions. Using findings from this study, practitioners can make review websites more user-friendly which will help readers avoid information overload and make more informed purchase decisions.

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Section: Literature Reviewmentioning

confidence: 99%

Perceived helpfulness of eWOM: Emotions, fairness and rationality

Ismagilova

Dwivedi

Slade

2020

Journal of Retailing and Consumer Services

Self Cite

102

View full text Add to dashboard Cite

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“…However, this task is a domain-based and expensive one. K. Kaushik et.al, concentrated on exploring reviews and review sequences on the e-commerce platform [10]. This work is intended for making the consumer know about the additional characteristics of a product other than statistically defined attributes.…”

Section: Related Workmentioning

confidence: 99%

A Filter Based Improved Decision Tree Sentiment Classification Model for RealTime Amazon Product Review Data

Syamala¹,

Nalini²

2020

IJIES

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E-Commerce product features and reviews are considered to be the essential factors in real-time e-commerce sites for product recommendation systems. Due to inaccuracy decision patterns, in most cases e-commerce user fails to predict the products based on the user ratings and review comments. Traditional sentiment classification models are independent of data filtering, transformation and sentiment score computing techniques which require high computing memory, time and mostly leading to false-positive rate. To overcome these issues, a novel sentiment score-based product recommendation model is proposed on the real-time product data. In this model, a new product ranking score, filtering, and hybrid decision tree classifiers are implemented. Initially, real-time amazon product review data is captured using Document Object Model (DOM) parser. The features from the review comments are extracted using lexicon Feature Dictionary (FD) and AFINN, Normalized Product Review Score (NPRS) are generated to compute the class label for product review sentiment prediction. Ranked Principal Component Analysis (RPCA) is used as a feature selection measure to overcome the problem of data sparsity. Random Tree, Hoeffding Tree, Adaboost + Random Tree, the three variants of decision tree classifiers are used for product sentiment classification. The proposed filter-based improved decision tree sentiment classification model for real-time amazon product review data recommends the product based on the user query by prediction using a new novel normalized product review sentiment score and ranked feature selection measure. The proposed product recommendation, the decision-making system maximizes sentiment classification accuracy. Experimental results are compared against the traditional decisionmaking classification models in terms of correctly classified instances, error rate, and PRC, F-measure, kappa statistics. The proposed model experimental results show high efficiency.

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“…In addition, the data in [20] collected from amazon.in platform for many products. They picked a product that has a useful participation such as electronics products.…”

Section: Attributes Fieldmentioning

confidence: 99%

“…In [17] the experiment done by using Fisher's Z and link effect sizes (i.e., correlation coefficients) by using Comprehensive Meta-Analysis (CMA) 2.0 software. While the authors in [20] used linear regression to test the correlations in proposed model. They ran the regression among the content of product page and following week sales of the product.…”

Section: Techniques Used In Review Helpfulness Predictionmentioning

confidence: 99%

“…Then it identified the effect degree of confidence level between friends and sentiment rating by using machine learning regression algorithms. In [20] framework was proposed for analyzing the impact of helpful reviews on product sales. Different characteristics of customer reviews and their impact on product sales are hypothesized in proposed framework.…”

Section: Framework Of Reviews Helpfulnessmentioning

confidence: 99%

See 1 more Smart Citation

Review helpfulness prediction: Survey

Almutairi¹,

Abdullah²,

Alahmadi³

2019

PEN

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Online reviews have become the major driving factor influencing purchasing behavior and patterns of social customers. However, it is difficult for customer to cover good reviews about any product or service according to massive amount of reviews latest years. Many previous researches provide innovative models about predicting review helpfulness in E-commerce websites. Some of these studies exploring the direct effect of review attributes on review helpfulness while others focused on reviewer's attributes only. The main objective of this research is to review the most important attributes that have an affect on review helpfulness from many perspectives such as datasets, techniques, frameworks and evaluation methods of the experiments. The paper ends up with important findings about most attributes effect the review helpfulness such as Review Valence.

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Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in

Cited by 80 publications

References 60 publications

Perceived helpfulness of eWOM: Emotions, fairness and rationality

Perceived helpfulness of eWOM: Emotions, fairness and rationality

A Filter Based Improved Decision Tree Sentiment Classification Model for RealTime Amazon Product Review Data

Review helpfulness prediction: Survey

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