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

Hu, Shigang; Kumar, Akshi; Al‐Turjman, Fadi; Gupta, Shivam; Seth, Simran; Shubham, .

doi:10.1109/access.2020.2971087

Cited by 72 publications

(26 citation statements)

References 42 publications

(54 reference statements)

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“…This is especially true recently, revealing the potential and topicality of the subject in many fields. This kind of analysis has been devoted, for instance, to consumer goods [14], [15], to brand recognition [16], to characterize political sentiment [17], [18], or to even real-time television scheduling of tv-programming [19], among others. But the area of stock markets and companies evaluation is probably the most intensively analyzed, as the titanic economic environment surrounding this specific topic encourages any potential new perspective that may provide tools or support for investor business cases.…”

Section: Introductionmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at the same time an interesting way to identify singularities among peers. We conclude that temporal properties of messages provide with information about the sentiment dynamics, which is different in terms of lexicons and users, but commonalities can be exploited in this field using appropriate temporal digital processing tools.

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Section: Introductionmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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“…Sentiment analysis is an often applied approach to understand online customer reviews of products/services such as mobile apps [23, 24] and E‐commerce systems [25–29]. Besides, Hu and Liu [30] have analysed sentiments in customer reviews to summarise the reviews.…”

Section: Related Workmentioning

confidence: 99%

“…Though the performance of the proposed work is not compared with the state of the art, but it is found effective as per the evaluation dataset of 5000 reviews. Hu et al [29] use reviewer credibility along with fine‐grained sentiment analysis to mitigate fake reviews of cameras on the ecommerce web site http://Amazon.com.…”

Section: Related Workmentioning

confidence: 99%

Customer reviews as the measure of software quality

Saini¹,

Chahal²,

Verma³

et al. 2020

IET softw.

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Open Source Software (OSS) has become ubiquitous nowadays. It is crucial for the OSS project managers as well as developers to understand users' perception of quality to remain consistent with producing good quality software. To understand users' point of view, like many studies in the commercial product/service sectors which rely upon customer reviews to understand customer behaviour, the authors' main focus is to analyse user ratings and reviews of OSS projects that may represent user satisfaction for that particular software application. We have analysed 41,428 customer reviews (obtained from SourceForge.net) of 886 most popular OSS projects belonging to a specific domain and programming language. The results indicate that overall user ratings and reviews of the popular OSS projects contain a very positive sentiment and more frequent occurrence of emotions like joy, anticipation, and trust as compared to disgust, fear, and surprise. Further, we have examined that the affectiveness of customer reviews with respect to OSS popularity and quality aspects along with their programming languages and problem domains. The results show a stronger association of review affectiveness with the number of reviews than with the number of downloads of the OSS projects, and more downloads do not mean more reviews.

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“…A recommendation model is proposed by combining CF and deep neural networks [21]. Moreover, product recommendation is proposed by considering reviewer credibility and sentiment analysis based User profile modelling [22].…”

Section: A Recommender Systems and Matrix Factorizationmentioning

confidence: 99%

Rating Prediction Based on Merge-CNN and Concise Attention Review Mining

et al. 2020

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Online review websites provide an open platform for users to write reviews or give ratings on items (business services) as well as share their consumption experience. However, the volume of reviews is large, while the rating scores provide users with a quick picture of the items without reading all reviews. Recommendation systems can help users find items of interest by predicting user's ratings on unrated items. Review contents contain more personalized preference features than simply user ratings. Therefore, it is important to consider both ratings and review contents when making rating predictions. This research proposes a novel approach that combines deep learning and review mining with attention mechanism for rating predictions. Review mining with attention mechanism is adopted to extract concise attention reviews with important words and sentences. A merge convolutional neural network (merge-CNN) model is proposed to consider both the target user's preference features and performance features of target business for rating prediction. This method extracts quality business performance features from the quality reviews written by elite (credible) users. Moreover, the proposed method uses the concise attention reviews of target user's neighbors to simulate target users' reviews on unrated target business. Experiments were conducted on Yelp data sets to evaluate our proposed methods. The results show that the proposed method, i.e. considering concise attention reviews and quality reviews written by elite users, outperforms traditional methods in improving prediction accuracy. The experiment result also shows that our review simulation methods can well simulate target user's reviews on unrated target business.

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

Cited by 72 publications

References 42 publications

On the Statistical and Temporal Dynamics of Sentiment Analysis

On the Statistical and Temporal Dynamics of Sentiment Analysis

Customer reviews as the measure of software quality

Rating Prediction Based on Merge-CNN and Concise Attention Review Mining

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