User and item-aware estimation of review helpfulness

Abstract

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.

Exploring the Impact of Linguistic Signals Transmission on Patients’ Health Consultation Choice: Web Mining of Online Reviews

“…Second, H2, H3, and H4 are supported significantly, which means that informative signals positively influence users’ decision-making process. These results are consistent with extant studies [ 46 , 49 , 76 ]. Informative signals include review readability, depth, and spelling.…”

“…Third, H5 is significantly supported, that is to say, patients’ negative sentiments about physician’s service quality have significant effects on perceived IH. The positive relation between user sentiments and IH has been tested before [ 48 , 76 ]. The results of the present study showed that negative emotions in reviews showed a positive effect on review helpfulness.…”

Beyond Traditional Cultural Heritage Recommender Systems: Suggesting Airbnb Experiences to Users

“…Our future work includes the development of the envisaged algorithms and their evaluation, offline and with real users. Moreover, we plan to enhance the extraction of aspects from reviews by using review helpfulness analysis [13,15,24,35] to select valuable consumer feedback and filter out low-quality reviews.…”

Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach

“…The proposed model was successfully validated, and critical components that would make an opinion relevant to readers were discovered. The study findings have added to related literature by offering more comprehension of the structural characteristics (quantitative and qualitative) of reviews and their effect on RH [ 18 , 19 , 31 ]. The findings indicate that both the review- and service-related signals significantly and positively influence perceived RH.…”

“…Fang et al [ 17 ] indicated that text readability significantly influences perceived RH. Mauro et al [ 18 ] revealed that review wordiness is a meaningful predictor of RH. The study of Malik and Hussain [ 19 ] indicated that discrete emotions are the most dominant emotions with greater influence on perceived RH.…”