Predicting the helpfulness of online customer reviews: The role of title features

Akbarabadi, Mina; Hosseini, Monireh

doi:10.1177/1470785318819979

Cited by 35 publications

(19 citation statements)

References 33 publications

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“…The length of review and the review rating were identified as key features [50]. Moreover, it has been stated that the title features did not have a significant effect on the usefulness of the review [51].…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

“…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%

“…Moreover, very limited business features have been studied in the literature. These business features include review volume, number of checkins, and business star rating [51], [52], [80], [82]. In addition, previous literature has reported the importance of different review content features and their influence on the helpfulness of online reviews.…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

“…In addition, previous literature has reported the importance of different review content features and their influence on the helpfulness of online reviews. The important features identified by previous studies includes age of review [11], [37], [51], [58], [73], [75], review rating [50], [58], [76], [83], number of words in a review [11], [50], [51], [58], [76], [83], readability of review using Automated Readability Index (ARI) [11], [39], [50], [51], [80], Polarity and Subjectivity of review [50], [51], [73], [83]. The summary of review helpfulness features is presented in Table 1.…”

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: 99%

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2020

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“…The proposed model was tested on a collected dataset of book reviews using ML algorithms i.e., Decision Tree (DT) and RandF. It was reported that the review title features were not a significant predictor of review helpfulness [42]. A model based on GB algorithm was proposed to predict review helpfulness by using textual features of reviews, i.e., readability, polarity, and subjectivity.…”

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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Learning successful implementation of Chatbots in businesses from B2B customer experience perspective

Kaushal

Yadav

2022

Concurrency and Computation

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Summary Artificial intelligence empowered Chatbots are altering the nature of service interfaces which has further resulted in raised expectations from Chatbots to understand customer's social interactions and respond them within the turnaround time. To close this research gap, we conduct an exploratory study in two phases‐industry's perspective and B2B customer's perspective and analyze results with the help of NVIVO 12 plus and Leximancer. The findings reveal perceived risk with respect to Chatbots is high, complex pricing structure along with nonavailability of testing options makes the pre purchase more complex. Moreover, interactive speed, customization especially with respect to language issues, integration with other platforms is some of the major themes which influence customer experience. Advancements in AI, natural language processing and more testing at all phases will bring efficiency, automation first strategies. Further, our findings suggest Chatbots must provide more personalization, scalability and omni channel engagement and focus on delivering more enhanced customer experience. Chatbots must offer a grievance management dashboard where the customer can see live queries, resolved queries, present queries status and so on to get transparency. Chatbots streamline the lead qualification process, greatly improve, and speed up the data collection therefore, enhancing customer experience.

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Predicting the helpfulness of online customer reviews: The role of title features

Cited by 35 publications

References 33 publications

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

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

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

Learning successful implementation of Chatbots in businesses from B2B customer experience perspective

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