Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales

Hu, Nan; Koh, Noi Sian; Reddy, Srinivas K.

doi:10.1016/j.dss.2013.07.009

Cited by 354 publications

(247 citation statements)

References 36 publications

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“…Moreover, customers tend to read not only review ratings but also review texts before making purchase decisions (Chevalier and Mayzlin, 2006). Such text sentiment can be seen as unique types of cognitive appraisals from previous customers, and such appraisals provide a useful information set for a prospective consumer's cognitive processing (Hu et al, 2014;Yin et al, 2014). Therefore, apart from the online review rating, text sentiment of an online review is also an important factor that may influence purchase decisions.…”

Section: Text Sentiment Of Online Reviewsmentioning

confidence: 99%

“…It is commonly assumed that the text sentiment of online reviews is consistent with and thus may be overlooked by a numeric review rating (Hu et al, 2014). However, a large proportion of reviews tend to have either extremely high numeric ratings or extremely low ones (Hu et al, 2006).…”

Section: Text Sentiment Of Online Reviewsmentioning

confidence: 99%

“…Although previous studies have examined the roles of review rating valence and volume of review (Ye et al, 2011), studies examining the roles of sentiments in online reviews remain sparse (Hu et al, 2014). In particular, the roles of valence and ratings are not independent of sentiments given that users are exposed to all these information on a website, and therefore the role of review sentiments' interactions with reviews ratings valance and volume need to be further investigated.…”

Section: Introductionmentioning

confidence: 99%

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Predicting online product sales via online reviews, sentiments, and promotion strategies

Chong

Ngai

et al. 2016

International Journal of Operations &Amp; Production Management

143

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Purpose – The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales. Design/methodology/approach – The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales. Findings – This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume. Originality/value – This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.

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Section: Text Sentiment Of Online Reviewsmentioning

confidence: 99%

Section: Text Sentiment Of Online Reviewsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting online product sales via online reviews, sentiments, and promotion strategies

Chong

Ngai

et al. 2016

International Journal of Operations &Amp; Production Management

143

View full text Add to dashboard Cite

show abstract

“…Therefore, the quantity of online product reviews is also growing rapidly in e-commerce websites with the rapid development of e-commerce. Now the online reviews play a very important role in consumer purchase decisions [1]. However, owing to factors concerning psychology, economy, institutions etcetera, there is a certain gap between review content and review score, which results in score deviation, further make consumers caught in a buying decision dilemma-being vague, contradictory and hesitating.…”

Section: Introductionmentioning

confidence: 99%

Correction of Online Product Scores from the Perspective of Engagement in the Appraisal Theory

Chen¹,

Song²

2015

Proceedings of the 2015 International Conference on Engineering Management, Engineering Education and Information Technology

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Abstract：Product reviews and scores have a direct impact on consumers' buying behavior. However, there are inconsistency between reviews and scores. Based on the linguistic Appraisal Theory from the perspective of engagement in the theory-construction index system, this article puts forward online product reviews-scores correction method. The method is applied to verify and correct the real product reviews data. The result shows that this method cannot only explain the differences between reviews and scores and reflect the real scores of the products, but also help consumers make better decisions and improve the network evaluation mechanisms.

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“…It can be classified according to two dimensions: length and subjectivity. Studies on strategic decision for marketing usually apply sentiment classification to short SNS messages (HennigThurau et al 2015), product reviews in e-commerce platform (Yang and Chao 2015;Hu et al 2014), or the Internet forums. News documents and financial columns, which are longer than social media messages and product reviews, can be used for financial decision support and risk assessment (Wu et al 2014).…”

Section: Selection Of Data Properties For Comparisonmentioning

confidence: 99%

Data properties and the performance of sentiment classification for electronic commerce applications

Choi

Lee

2017

Inf Syst Front

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Sentiment classification has played an important role in various research area including e-commerce applications and a number of advanced Computational Intelligence techniques including machine learning and computational linguistics have been proposed in the literature for improved sentiment classification results. While such studies focus on improving performance with new techniques or extending existing algorithms based on previously used dataset, few studies provide practitioners with insight on what techniques are better for their datasets that have different properties. This paper applies four different sentiment classification techniques from machine learning (Naïve Bayes, SVM and Decision Tree) and sentiment orientation approaches to datasets obtained from various sources (IMDB, Twitter, Hotel review, and Amazon review datasets) to learn how different data properties including dataset size, length of target documents, and subjectivity of data affect the performance of those techniques. The results of computational experiments confirm the sensitivity of the techniques on data properties including training data size, the document length and subjectivity of training /test data in the improvement of performances of techniques. The theoretical and practical implications of the findings are discussed.

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Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales

Cited by 354 publications

References 36 publications

Predicting online product sales via online reviews, sentiments, and promotion strategies

Predicting online product sales via online reviews, sentiments, and promotion strategies

Correction of Online Product Scores from the Perspective of Engagement in the Appraisal Theory

Data properties and the performance of sentiment classification for electronic commerce applications

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