A study of factors that contribute to online review helpfulness

Huang, Albert; Chen, Kuanchin; Yen, David C.; Tran, Trang P.

doi:10.1016/j.chb.2015.01.010

Cited by 298 publications

(263 citation statements)

References 67 publications

(105 reference statements)

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“…This hypothesis was however not supported by the study of Baek et al [24] and was even contradicted by the study of Pan and Zhang [32] who found that positive reviews have a greater probability of being rated helpful than negative ones and that the helpfulness decreases with decreased rating. This is also supported by the study of Korfiatis et al [22] who found that helpfulness is affected by the positive rating value, by the study of Huang et al [29] who found that for top reviewers, positive reviews are more likely to be helpful, and by the study of Schindler and Bickart. [26] who found that greater proportions of positive statements are associated with high value reviews.…”

Section: B Factorssupporting

confidence: 69%

“…This relation holds until a certain length threshold (1000 to 1500 words according to [24]) which seems to depend on the type of product. After the threshold, the helpfulness starts decreasing [24][23] [29], which could be due to the readers disregarding very long reviews [23]. The effect of length on helpfulness has been identified for various types of products and seems to have greater effect for search goods 2 than for experience goods 3 [30] [32].…”

Section: B Factorsmentioning

confidence: 99%

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Which One to Read? Factors Influencing the Usefulness of Online Reviews for RE

Charrada¹

2016

2016 IEEE 24th International Requirements Engineering Conference Workshops (REW)

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Abstract-Reviews for software products contain much information about the users' requirements and preferences, which can be very useful to the requirements engineer. However, taking advantage of this information is not easy due to the large and overwhelming number of reviews that is posted in various channels. Machine learning and opinion mining techniques have therefore been used to process the reviews automatically and to generate summaries of the data to the requirements engineer. However, one of the important challenges for these techniques lies in how to automatically assess the relevance of the reviews for the requirements engineer. So far, most techniques use intuitionbased criteria for this task. In this work, we collect and present a list of factors that were found to impact the helpfulness of product reviews for customers. We then discuss to what extent these factors are likely to impact the usefulness of reviews for requirements engineering tasks. The factors can be used to support the automated identification of relevant reviews.

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Section: B Factorssupporting

confidence: 69%

Section: B Factorsmentioning

confidence: 99%

Which One to Read? Factors Influencing the Usefulness of Online Reviews for RE

Charrada¹

2016

2016 IEEE 24th International Requirements Engineering Conference Workshops (REW)

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“…Specifically, the goal of the study is to examine the joint effect of the message length of a review (word count) together with reviewer characteristics and the patterns of the review on the utility of the review. While prior studies have suggested that there is a positive correlation between word count and the perceived helpfulness of a review [52,77], the results of this study point out that the association between word count and helpfulness is valid only in reviews with 144 or less words [82]. The hypothesis results of this study are listed in table 5.…”

Section: Categorysupporting

confidence: 56%

“…Recently, a study of factors contributing to online review helpfulness was carried out [82]. Specifically, the goal of the study is to examine the joint effect of the message length of a review (word count) together with reviewer characteristics and the patterns of the review on the utility of the review.…”

Section: Categorymentioning

confidence: 99%

A Survey of Quality Prediction of Product Reviews

Almagrabi¹,

Malibari²,

McNaught³

2015

ijacsa

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Abstract-With the help of Web-2.0, the Internet offers a vast amount of reviews on many topics and in different domains. This has led to an explosive growth of product reviews and customer feedback, which presents the problem of how to handle the abundant volume of data. It is an expensive and time-consuming task to analyze this huge content of opinions. Therefore, the need for automated sentiment analysis systems is vital. However, these systems encounter many challenges; assessing the content quality of the posted opinions is an important area of study that is related to sentiment analysis. Currently, review helpfulness is assessed manually; however the task of automatically assessing it has gained more attention in recent years. This paper provides a survey of approaches to the challenge of identifying the content quality of product reviews.

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“…(1) The number of online reviews By summarizing the study of iwom, we find that the key objective characteristics of online reviews (number and rating) have influence on iwom perception [3]. The number of online reviews decides the number of consumers who participate in discussing about product.…”

Section: 1the Quantifiable Indicatorsmentioning

confidence: 99%

Research on Influence Factors of Quantitative Perception of Internet Word of Mouth Based on Online Reviews

Li¹,

Ma²,

Zhu³

et al. 2017

Proceedings of the 7th International Conference on Computer Engineering and Networks — PoS(CENet2017)

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In order to accurately measure the value of internet word of mouth (iwom). Based on each attribute of online reviews, which are the major form of iwom, we proposes the influence factors of iwom, include the quantifiable indicators: the number of online reviews, the rating, the proportion of negative reviews, the price of product, and the unquantifiable indicators which can control of the false online reviews and express consumer emotion: the feature words of product attributes, the emotional intensity of reviews. Then we build the model of influence factors of iwom perception. And we take the influence of endogenous into account. Before and after the control of the endogenous influence, we find that the relationship between influence factors and iwom has changed by using cross section analysis and first order difference analysis.

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A study of factors that contribute to online review helpfulness

Cited by 298 publications

References 67 publications

Which One to Read? Factors Influencing the Usefulness of Online Reviews for RE

Which One to Read? Factors Influencing the Usefulness of Online Reviews for RE

A Survey of Quality Prediction of Product Reviews

Research on Influence Factors of Quantitative Perception of Internet Word of Mouth Based on Online Reviews

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