Exploring eWOM in online customer reviews: Sentiment analysis at a fine-grained level

Sun, Qing; Niu, Jianwei; Yao, Zhong; Yan, Hao

doi:10.1016/j.engappai.2019.02.004

Cited by 70 publications

(33 citation statements)

References 22 publications

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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%

See 1 more Smart Citation

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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Section: Related Workmentioning

confidence: 99%

“…Sun et al [25] perform sentiment analysis at fine‐grained level to extract product features and sentiments from customer reviews of mobile phones from social media sites. 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.…”

Section: Related Workmentioning

confidence: 99%

Customer reviews as the measure of software quality

Saini¹,

Chahal²,

Verma³

et al. 2020

IET softw.

View full text Add to dashboard Cite

show abstract

“…Customer reviews [15] Develop a fine-grained SA supervised by semantic knowledge, context sensitive sentiments are extracted from online customer reviews. Customer reviews [16] A case of TM on Airbnb user reviews to analyze and understand various aspects that drive customer satisfaction. Customer reviews [17] Explore and decode the sentiment dynamics of Twitter users regarding online retailing brands.…”

Section: Research Objectives Of the Study Crd * Analyzed R **mentioning

confidence: 99%

Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences

Păvăloaia

Teodor²,

Fotache

et al. 2019

Sustainability

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Any brand’s presence on social networks has a significant impact on emotional reactions of its users to different types of posts on social media (SM). If a company understands the preferred types of posts (photo or video) of its customers, based on their reactions, it could make use of these preferences in designing its future communication strategy. The study examines how the use of SM technology and customer-centric management systems could contribute to sustainable business development of companies by means of social customer relationship management (sCRM). The two companies included in the study provide a general consumer good in the beverage industry. As such, it may be said that users interacting with the posts these companies make on their official channels are in fact customers or potential customers. The study aims to analyze customer reaction to two types of posts (photos or videos) on six social networks: Facebook, Twitter, Instagram, Pinterest, Google+ and Youtube. It brings evidence on the differences and similarities between the SM customer behaviors of two highly competitive brands in the beverage industry. Drawing on current literature on SM, sCRM and marketing, the output of this study is the conceptualization and measurement of a brand’s SM ability to understand customer preferences for different types of posts by using various statistical tools and the sentiment analysis (SA) technique applied to big sets of data.

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“…The researchers suggested numerous methodologies that evaluated reviews thereby improved the ratings of recommendations for the desired product. The various methodologies were used for analyzing the reviews based on sentimental analysis that recommended the products for customers based on their reviews [7,8].…”

Section: Introductionmentioning

confidence: 99%

Accelerated - Generic Gradient Descent for E-Commerce Recommender Systems

Suresh¹,

Belinda²

2021

INDJCSE

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The online product reviews play an important role in the field of e-commerce as rating of the product infers the preference for customers and they rely on them while making purchases. The main aim of providing an efficient recommender system is to gain useful information for user based on quality, worth and quantity of the product. The reviews given by the customers are used to extract the features of products that evaluate the similarity content reviewed by another customer. Therefore, the reviews written by the distinct persons would be similar, but with different words of expression shows difficulty during review differentiation. The developed model performed mining customer review for a set of manufactured goods and their characteristics are used as product features. The proposed Gated Recurrent Unit with Accelerated -Generic Gradient Descent (GRU-AGGD) technique extracts the relevant and sufficient features from the review to overcome the existing optimization problems. These relevant features are the summary of the product used to predict the rank using the users-attributes and users-items matrix and these two matrixes give rise to Non-Negative Matrix Factorization model to provide recommendations to user for the product. The experimental results of product recommender system are evaluated using the proposed GRU-AGGD that shows the improvement in terms of performance measures. The average MSE and RMSE values obtained for the proposed GRU-AGGD technique are 0.84 and 0.91 respectively, which are higher when compared to the Bayesian recommender models.

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Exploring eWOM in online customer reviews: Sentiment analysis at a fine-grained level

Cited by 70 publications

References 22 publications

Customer reviews as the measure of software quality

Customer reviews as the measure of software quality

Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences

Accelerated - Generic Gradient Descent for E-Commerce Recommender Systems

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