Do reviewers’ words affect predicting their helpfulness ratings? Locating helpful reviewers by linguistics styles

Li, Sheng-Tun; Pham, Thuong Thi; Chuang, Hui Chi

doi:10.1016/j.im.2018.06.002

Cited by 53 publications

(37 citation statements)

References 35 publications

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“…A examined the effect of writing style on review helpfulness using four linguistic features. The linguistic features appeared to be more significant than those of the social relationship features [54]. Moreover, the studies also highlighted the important textual features such as polarity, subjectivity, readability, etc., for predicting the helpfulness of reviews [55], [56].…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 86%

“…The reviewer popularity and experience feature, i.e. number of compliments [52], number of friends [52], number of fans [52], number of reviews [38], [50]- [53], [68], [77], useful votes [38], [50]- [53], [75], [80], average useful votes [53], [77], credibility [39], [50], [51], [54], [78], recency [50], [53], frequency [50], [53], monetary [50], [53] and country [51] has been listed in literature. Whereas very few studies have attempted to explore the relationship between the reviewer's rating behavior and helpfulness of their reviews [29], [63].…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

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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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Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 86%

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Language expression says a lot about the intent of reviewer and quality of the review on the whole. As Sheng Tun Li [4] mentions in his work, several linguistic indicators like capitalisation, emoticons, spelling errors, semantics, regularity, consistency etc. play a major role in devising user credibility.…”

Section: ) Review Contentmentioning

confidence: 99%

“…Pertinent research studies suggest various parameters that can affect reviewer credibility. These include: linguistic styles, review clarity and comprehensiveness, word count, sentence count in reviews, helpful vote count, evidence (speaker's degree of certainty using certain propositional attitudes for example, certainly, surely) [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

Kumar

Al‐Turjman³

et al. 2020

IEEE Access

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Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules namely candidate feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment and recommendation module. Review corpus is given as an input to the CISER model. Candidate feature extraction module uses context and sentiment confidence to extract features of importance. To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their opinion according to their credibility. The user interest mining module uses aesthetics of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module scores candidate features present in review based on their fastText sentiment polarity. Finally, the recommendation module uses credibility weighted sentiment scoring of user preferred features for purchase recommendations. The proposed recommendation methodology harnesses not only numeric ratings, but also sentiment expressions associated with features, customer preference profile and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93% and MAP@3 is 49%, which is better than current state-of-the-art systems.

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“…The stylistic features were reported a good predictor of review helpfulness in comparison with other features. However, it was suggested to use the stylistic features along with social features to gain better performance [53]. Krishnamoorthy [5] proposed a predictive model to investigate the review features that have an impact on reviews helpfulness.…”

Section: Literature Reviewmentioning

confidence: 99%

Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews

et al. 2019

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With easy access to the Internet and the popularity of online review platforms, the volume of crowd-sourced reviews is continuously rising. Many studies have acknowledged the importance of reviews in making purchase decisions. The consumer’s feedback plays a vital role in the success or failure of a business. The number of studies on predicting helpfulness and ranking reviews is increasing due to the increasing importance of reviews. However, previous studies have mainly focused on predicting helpfulness of “reviews” and “reviewer”. This study aimed to profile cumulative helpfulness received by a business and then use it for business ranking. The reliability of proposed cumulative helpfulness for ranking was illustrated using a dataset of 1,92,606 businesses from Yelp.com. Seven business and four reviewer features were identified to predict cumulative helpfulness using Linear Regression (LNR), Gradient Boosting (GB), and Neural Network (NNet). The dataset was subdivided into 12 datasets based on business categories to predict the cumulative helpfulness. The results reported that business features, including star rating, review count and days since the last review are the most important features among all business categories. Moreover, using reviewer features along with business features improves the prediction performance for seven datasets. Lastly, the implications of this study are discussed for researchers, review platforms and businesses.

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Do reviewers’ words affect predicting their helpfulness ratings? Locating helpful reviewers by linguistics styles

Cited by 53 publications

References 35 publications

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

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

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews

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