Does the review deserve more helpfulness when its title resembles the content? Locating helpful reviews by text mining

Zhou, Yusheng; Yang, Shuiqing; Li, Yixiao; Chen, Yuangao; Yao, JingTao; Qazi, Atika

doi:10.1016/j.ipm.2019.102179

Cited by 74 publications

(51 citation statements)

References 63 publications

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“…Table 1 summarizes these factors by reporting, for each one, the impact on perceived helpfulness and the works supporting the finding. Most of the cited Yang et al (2015Yang et al ( , 2016 coherence (consistency) consistency between review polarity and rating, similarity between review title and content -, negative Dong et al (2013), Zhou et al (2020), Shen et al (2019) rating deviation deviation of rating from mean product rating positive Raghavan et al (2012) domain-specific content domain-specific item properties moderated by product Ahmad & Laroche 2017age number of days since publication positive Hong et al (2017), (Hu & Chen, 2016) works study a larger set of determinants but Table 1 only shows those identified as influential by the authors of the cited works.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Also (Dong et al, 2013) investigated this feature but they have not described its impact on perceived helpfulness. Zhou et al (2020) have looked at consistency from a different perspective and they discovered that the similarity between review title and review content positively influences perceived helpfulness. Raghavan et al (2012) found that review length and the deviation of the rating from the mean rating of the product are strong helpfulness predictors.…”

Section: Consistency and Rating Deviationsmentioning

confidence: 99%

See 1 more Smart Citation

User and item-aware estimation of review helpfulness

Mauro

Ardissono

Petrone

2021

Information Processing & Management

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In online review sites, the analysis of user feedback for assessing its helpfulness for decision-making is usually carried out by locally studying the properties of individual reviews. However, global properties should be considered as well to precisely evaluate the quality of user feedback. In this paper we investigate the role of deviations in the properties of reviews as helpfulness determinants with the intuition that "out of the core" feedback helps item evaluation. We propose a novel helpfulness estimation model that extends previous ones with the analysis of deviations in rating, length and polarity with respect to the reviews written by the same person, or concerning the same item. A regression analysis carried out on two large datasets of reviews extracted from Yelp social network shows that user-based deviations in review length and rating clearly influence perceived helpfulness. Moreover, an experiment on the same datasets shows that the integration of our helpfulness estimation model improves the performance of a collaborative recommender system by enhancing the selection of high-quality data for rating estimation. Our model is thus an effective tool to select relevant user feedback for decision-making.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Consistency and Rating Deviationsmentioning

confidence: 99%

User and item-aware estimation of review helpfulness

Mauro

Ardissono

Petrone

2021

Information Processing & Management

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“…It has been shown that the emotional tone of the reviews significantly influences the helpfulness of the reviews written by females, while no effect has been reported for male reviews [71]. A positive relationship between review helpfulness and similarity between review content and the title has been reported for Amazon reviews [72], [73]. A study suggested that the helpfulness of the reviews varies depending on the hotel class [74].…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

et al. 2020

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The increasing volume of online reviews and the use of review platforms leave tracks that can be used to explore interesting patterns. It is in the primary interest of businesses to retain and improve their reputation. Reviewers, on the other hand, tend to write reviews that can influence and attract people's attention, which often leads to deliberate deviations from past rating behavior. Until now, very limited studies have attempted to explore the impact of user rating behavior on review helpfulness. However, there are more perspectives of user behavior in selecting and rating businesses that still need to be investigated. Moreover, previous studies gave more attention to the review features and reported inconsistent findings on the importance of the features. To fill this gap, we introduce new and modify existing business and reviewer features and propose a user-focused mechanism for review selection. This study aims to investigate and report changes in business reputation, user choice, and rating behavior through descriptive and comparative analysis. Furthermore, the relevance of various features for review helpfulness is identified by correlation, linear regression, and negative binomial regression. The analysis performed on the Yelp dataset shows that the reputation of the businesses has changed slightly over time. Moreover, 46% of the users chose a business with a minimum of 4 stars. The majority of users give 4-star ratings, and 60% of reviewers adopt irregular rating behavior. Our results show a slight improvement by using user rating behavior and choice features. Whereas, the significant increase in R 2 indicates the importance of reviewer popularity and experience features. The overall results show that the most significant features of review helpfulness are average user helpfulness, number of user reviews, average business helpfulness, and review length. The outcomes of this study provide important theoretical and practical implications for researchers, businesses, and reviewers. INDEX TERMS Business reputation, rating behavior, review helpfulness, user profiling, crowd-sourced reviews.

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“…On the one hand, twothirds of the studies in our sample identified that positive sentiments in OCR texts have a positive impact on perceived helpfulness. On the other hand, some reported a negativity bias effect, with texts embedding negative sentiments being shown as more likely to be perceived as more helpful [36,37,48].…”

Section: Inconsistent Findingsmentioning

confidence: 99%

What Makes Consumer Perception of Online Review Helpfulness: Synthesizing the Past to Guide Future Research

Fan¹

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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A growing body of academic research has aimed to investigate the helpfulness of online customer reviews (OCRs) given their prevalence and the need to better understand their appraisal mechanisms. However, past studies have applied varied methods and reported conflicting findings. This study aims to improve the understanding of the contributors to OCR helpfulness by synthesizing past studies on the topic. Based on a systematic literature review, a summary of the precursors to OCR helpfulness is provided. We decipher both the consistent and conflicting results and discuss the possible explanations for these mixed findings. By summarizing past studies, the review also points out possible directions for future research.

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Does the review deserve more helpfulness when its title resembles the content? Locating helpful reviews by text mining

Cited by 74 publications

References 63 publications

User and item-aware estimation of review helpfulness

User and item-aware estimation of review helpfulness

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

What Makes Consumer Perception of Online Review Helpfulness: Synthesizing the Past to Guide Future Research

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