scholarly journals Spammer group detection and diversification of customers’ reviews

2021 ◽  
Vol 7 ◽  
pp. e472
Author(s):  
Naveed Hussain ◽  
Hamid Turab Mirza ◽  
Abid Ali ◽  
Faiza Iqbal ◽  
Ibrar Hussain ◽  
...  
Keyword(s):  
Real World ◽  
Online Reviews ◽  
Direct Impact ◽  
Review System ◽  
Detection Problem ◽  
Review Time ◽  
Public Opinions ◽  
Customer Reviews ◽  
Product Features ◽  
Group Detection

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profit or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, Spam Review Detection problem (SRD) has gained much attention from researchers, but still there is a need to identify review spammers who often work collaboratively to promote or demote targeted products. It can severely harm the review system. This work presents the Spammer Group Detection (SGD) method which identifies suspicious spammer groups based on the similarity of all reviewer’s activities considering their review time and review ratings. After removing these identified spammer groups and spam reviews, the resulting non-spam reviews are displayed using diversification technique. For the diversification, this study proposed Diversified Set of Reviews (DSR) method which selects diversified set of top-k reviews having positive, negative, and neutral reviews/feedback covering all possible product features. Experimental evaluations are conducted on Roman Urdu and English real-world review datasets. The results show that the proposed methods outperformed the existing approaches when compared in terms of accuracy.

Mining opinionated product features using WordNet lexicographer files

2016 ◽  
Vol 43 (6) ◽  
pp. 769-785 ◽  
Author(s):  
Saif A. Ahmad Alrababah ◽  
Keng Hoon Gan ◽  
Tien-Ping Tan
Keyword(s):  
Assessment Tool ◽  
Empirical Evaluation ◽  
Online Reviews ◽  
Product Reviews ◽  
Specific Domain ◽  
Customer Reviews ◽  
Product Features ◽  
Online Customer Reviews ◽  
Customer Perspective ◽  
Massive Number

Online customer reviews are an important assessment tool for businesses as they contain feedback that is valuable from the customer perspective. These reviews provide a significant basis on which potential customers can select the product that best meets their preferences. In online reviews, customers describe positive or negative experiences with a product or service or any part of it (i.e. features). Consumers frequently experience difficulty finding the desired product for comparison because of the massive number of online reviews. The automatic extraction of important product features is necessary to support customers in search of relevant product features. These features are the criteria that make it possible for customers to characterise different types of products. This article proposes a domain independent approach for identifying explicit opinionated features and attributes that are strongly related to a specific domain product using lexicographer files in WordNet. In our approach, N_gram analysis and the SentiStrength opinion lexicon have been employed to support the extraction of opinionated features. The empirical evaluation of the proposed system using online reviews of two popular datasets of supervised and unsupervised systems showed that our approach achieved competitive results for feature extraction from product reviews.

Predicting Future Importance of Product Features Based on Online Customer Reviews

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Huimin Jiang ◽  
C. K. Kwong ◽  
K. L. Yung
Keyword(s):  
Time Series ◽  
Opinion Mining ◽  
Fuzzy Inference ◽  
Online Reviews ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Customer Reviews ◽  
Product Features ◽  
Online Customer Reviews ◽  
Time Series Method

Previous studies conducted customer surveys based on questionnaires and interviews, and the survey data were then utilized to analyze product features. In recent years, online customer reviews on products became extremely popular, which contain rich information on customer opinions and expectations. However, previous studies failed to properly address the determination of the importance of product features and prediction of their future importance based on online reviews. Accordingly, a methodology for predicting future importance weights of product features based on online customer reviews is proposed in this paper which mainly involves opinion mining, a fuzzy inference method, and a fuzzy time series method. Opinion mining is adopted to analyze the online reviews and extract product features. A fuzzy inference method is used to determine the importance weights of product features using both frequencies and sentiment scores obtained from opinion mining. A fuzzy time series method is adopted to predict the future importance of product features. A case study on electric irons was conducted to illustrate the proposed methodology. To evaluate the effectiveness of the fuzzy time series method in predicting the future importance, the results obtained by the fuzzy time series method are compared with those obtained by the three common forecasting methods. The results of the comparison show that the prediction results based on fuzzy time series method are better than those based on exponential smoothing, simple moving average, and fuzzy moving average methods.

A Systematic Methodology Based on Word Embedding for Identifying the Relation Between Online Customer Reviews and Sales Rank

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Dedy Suryadi ◽  
Harrison Kim
Keyword(s):  
Online Reviews ◽  
Word Embedding ◽  
Data Sets ◽  
Alternative Evaluation ◽  
Customer Reviews ◽  
Product Features ◽  
Online Customer Reviews ◽  
Dependency Tree ◽  
Systematic Methodology ◽  
Source Of Information

In the buying decision process, online reviews become an important source of information. They become the basis of evaluating alternatives before making purchase decision. This paper proposes a methodology to reveal one of the hidden alternative evaluation processes by identifying the relation between the observable online customer reviews and sales rank. This methodology applies a combined approach of word embedding (word2vec) and X-means clustering, which produces product-feature words. It is followed by identifying sentiment words and their intensity, determining connection of words from dependency tree, and finally relating variables from the reviews to the sales rank of a product by a regression model. The methodology is applied to two data sets of wearable technology and laptop products. As implied by the high predicted R-squared values, the models are generalizable into new data sets. Among the interesting findings are the statements of problems or issues of a product are related to better sales rank, and many product features that are mentioned in the review title are significantly related to sales rank. For product designers, the significant variables in the regression models suggest the possible product features to be improved.

Customer Online Feedback with an Identity Versus No Identity: The Influence on Review Comments

2021 ◽  
pp. 109634802098888
Author(s):  
Dan Jin ◽  
Robin B. DiPietro ◽  
Nicholas M. Watanabe
Keyword(s):  
Online Community ◽  
Online Reviews ◽  
Purchasing Behavior ◽  
Mediation Effects ◽  
Driven Systems ◽  
Customer Reviews ◽  
Significant Difference ◽  
Online Feedback ◽  
The Relationship

As customers’ consumption is increasingly dominated by technology-driven systems, online self-verification becomes an important aspect of customers’ online purchasing behavior and plays a significant role in shaping social interactions in the online community. Across two studies, we examine whether online self-verification with an identity versus without an identity will lead to the different quality of online reviews. Study 1 used topic modeling with actual data stripped from Facebook and TripAdvisor customer online review sites and showed no difference between customer reviews underpinned with an identity or without. Likewise, Study 2 used an experimental design and found no significant difference between customer reviews with or without an identity. However, significant mediation effects of social ties and social capital were found when measuring the relationship between online self-verification and customer reviews. The findings build on the literature of user-generated online reviews and have important implications for academics and hospitality practitioners.

scholarly journals Examination of Early Feedbacks for Effective Product Retailing on E-Commerce Websites

2019 ◽  
Vol 8 (6S2) ◽  
pp. 703-706
Keyword(s):  
Real World ◽  
Online Reviews ◽  
Rating Score ◽  
Essential Information ◽  
Information Base ◽  
Behavioral Features ◽  
Suggested Strategy ◽  
The Creation ◽  
Effective Product ◽  
Median Rating

Online reviews became a essential information base for customers prior to the creation of the buy call affiliate. Early item reviews tend to have a strong effect on the following item revenues. Throughout this article, we tend to take the action to check early reviewers ' behavioral features through their announcement videos on two real-world gigantic e-commerce platforms, i.e., Amazon. Specifically, we tend to split the item cycle into three successive phases, especially early, majority and laggards. A person who published a review early on is considered as an early review associate We tend to quantitatively characterize early critics who have endorsed their ranking behaviors, the helpfulness results obtained from others, and hence the correlation between their ratings and the performance of the item. We discovered that combine early reviewers tend to give a stronger median rating score; linked with[ 2] early reviewers tend to publish more helpful feedback. Additionally, our item reviews assessment shows the ratings of these early reviewers and their earned helpfulness scores square measure that can affect the performance of the item. By watching the posting technique of evaluation as a competitive multiplayer game, we tend to suggest a totally distinctive embedding model for early reviewer prediction. Intensive tests on 2 completely distinct e-commerce datasets have shown that our suggested strategy exceeds various competitive baselines

scholarly journals A method of constructing syllable level Tibetan text classification corpus

2021 ◽  
Vol 336 ◽  
pp. 06013
Author(s):  
Jizhaxi Dao ◽  
Zhijie Cai ◽  
Rangzhuoma Cai ◽  
Maocuo San ◽  
Mabao Ban
Keyword(s):  
Empirical Evidence ◽  
Text Classification ◽  
Real World ◽  
Processing Technique ◽  
Construction Method ◽  
Direct Impact ◽  
Corpus Construction ◽  
Starting Point ◽  
Real World Applications ◽  
The Future

Corpus serves as an indispensable ingredient for statistical NLP research and real-world applications, therefore corpus construction method has a direct impact on various downstream tasks. This paper proposes a method to construct Tibetan text classification corpus based on a syllable-level processing technique which we refer as TC_TCCNL. Empirical evidence indicates that the algorithm is able to produce a promising performance, which may lay a starting point for research on Tibetan text classification in the future.

Determining banking service attributes from online reviews: text mining and sentiment analysis

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Divya Mittal ◽  
Shiv Ratan Agrawal
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Banking Sector ◽  
Online Reviews ◽  
Socioeconomic Background ◽  
Content Type ◽  
Customer Reviews ◽  
Banking Service ◽  
The Interest Rate

PurposeThe current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.Design/methodology/approachA total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.FindingsThe study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.Research limitations/implicationsThe study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.Practical implicationsThe study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.Originality/valueThis paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

Validating Perceived Sustainable Design Features Using a Novel Collage Approach

2021 ◽  
Author(s):  
Nasreddine El-Dehaibi ◽  
Ting Liao ◽  
Erin F. MacDonald
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Sustainable Design ◽  
Design Method ◽  
Online Reviews ◽  
Design Features ◽  
Customer Perceptions ◽  
Product Features ◽  
Social Environmental

Abstract Fierce e-commerce competition challenges designers to differentiate their products on platforms such as Amazon. To achieve this differentiation, designers must first understand how customers perceive product features. This paper builds on our previous work where we extracted features perceived as sustainable for French Press coffee carafes using annotations of Amazon reviews and natural language processing (NLP). We identified a gap between customer perceptions of sustainability and engineered sustainability. We now test our findings with a relatively new design method of collage placement and investigate how designers can use perceived features to set their products apart. We created collage activities for participants to evaluate French Press products on the three aspects of sustainability: social, environmental, and economic, and on how much they like the products. During the activity participants placed products along the two axes of the collage, sustainability and likeability, and labeled products with descriptive features that we provided. We found that participants more often selected features perceived as sustainable when placing products higher on the sustainability axis, demonstrating that these features resonated with customers. We also measured a low correlation between the two-axes of the collage activity, indicating that perceived sustainability and likeability can be measured separately. In addition, we found that product perceptions across sustainability aspects may differ between demographics. Based on these results, we confirm that features perceived as sustainable that are extracted from online reviews resonate with customers when thinking of various sustainability aspects and that the collage is an effective tool for assessing sustainability perceptions.

Intelligent Kano classification of product features based on customer reviews

CIRP Annals ◽  
2019 ◽  
Vol 68 (1) ◽  
pp. 149-152 ◽  
Author(s):  
Diandi Chen ◽  
Dawen Zhang ◽  
Ang Liu
Keyword(s):  
Customer Reviews ◽  
Product Features

Product Quality Assessment Based on Online Reviews

2019 ◽  
Vol 11 (3) ◽  
pp. 81-97
Author(s):  
Chao Li ◽  
Jun Xiang ◽  
Shiqiang Chen
Keyword(s):  
Quality Assessment ◽  
Product Quality ◽  
Opinion Mining ◽  
Online Reviews ◽  
Product Reviews ◽  
Product Features ◽  
Related Quality ◽  
Quality Dimensions

Reviews can reflect the degree of consumers' satisfaction and views on product quality, and consumers tend to read product reviews and then get helpful information about product quality before placing an order in e-commerce platforms. However, the existing research mainly focus on the assessment of review quality, fake review detection, opinion mining, and there is little research to assess product quality from the perspectives of product features based on reviews objectively and quantifialy. Therefore, the authors propose a method to assess product quality based on reviews in a granularity of product feature. The authors define the related quality dimensions and develop the corresponding assessment models, assess the review quality crawled from an e-commerce platform, then extract product features and opinion words from the quality reviews, and finally assess product quality on the extracted and consumer-concerned features. Experiment results demonstrate the methodology can achieve the assessment of product quality on any feature objectively and quantificationally.

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