Apect-Based Opinion Extraction From Customer Reviews

Samha, Amani K.; Li, Yuefeng; Zhang, Jinglan

doi:10.5121/csit.2014.4413

Cited by 29 publications

(23 citation statements)

References 13 publications

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“…In e-commerce, Web 2.0 provides platforms for the internet users to share their knowledge, expertise and experiences on forums, review portals, blogs and other social media websites (Bertola and Patti (2016)). These platforms facilitate the users to express their personal feelings, opinions, experiences and attitudes not only for political and economic problems but also for products and services (Samha, Li et al (2014)). Product reviews are acknowledged in the previous studies (Crowley and Hoyer (1994); Chua and Banerjee (2016)).…”

Section: Introductionmentioning

confidence: 99%

Helpfulness of product reviews as a function of discrete positive and negative emotions

Malik

Hussain

2017

Computers in Human Behavior

141

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The product review plays an important role in customer's purchase decision making process on the e-commerce websites. Emotions can significantly influence the way that reviews are processed. The importance of discrete emotions embedded in online reviews and their impact on review helpfulness is not explored intensively in prior studies. This study builds a helpfulness predictive model using deep neural network and investigates the influences of emotions that contribute to review helpfulness. We present an approach that extract novel discrete positive and negative emotion features from textual content of product reviews using NRC emotion Lexicon. In addition, the type of product, reviewer, visibility, readability, linguistics and sentiment related characteristics are also used for comparison and helpfulness prediction. The experimental results on two real-life datasets demonstrate that positive emotion features are the best predictors when individual category of feature is considered. However, negative emotion features and visibility features have comparable performance. Furthermore, the hybrid set of features with positive emotion features produce the best predictive performance for helpfulness of online reviews. The empirical evaluation finds that Trust, Joy and Anticipation (positive emotions); Anxiety and Sadness (negative emotions) are most influential emotion dimensions and have greater impact on perceived helpfulness. The findings of this study highlight the importance of emotions in online reviews and have significant implications for consumer and ecommerce retailers.

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Section: Introductionmentioning

confidence: 99%

Helpfulness of product reviews as a function of discrete positive and negative emotions

Malik

Hussain

2017

Computers in Human Behavior

141

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“…The aim of these reviews is to help consumers in making the buying decision easier. These reviews are used as tool; for improvement in products and services benchmarking, market intelligence strategies, and also for policy making by businesses and organizations [1].…”

Section: Id:p0110mentioning

confidence: 99%

Enactment of Ensemble Learning for Review Spam Detection on Selected Features

Khurshid¹,

Zhu²,

Xu³

et al. 2019

IJCIS

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In the ongoing era of flourishing e-commerce, people prefer online purchasing products and services to save time. These online purchase decisions are mostly influenced by the reviews/opinions of others who already have experienced them. Malicious review spam. This study aims to evaluate the performance of ensemble learning on review spam detection with selected features extracted from real and semi-real-life datasets. We study various performance metrics including Precision, Recall, F-Measure, and Receiver Operating Characteristic (RoC). Our proposed ensemble learning module (ELM) with ChiSquared feature selection technique outperformed all others with 0.851 Precision.

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“…Smaller, you can build a dictionary of aspects for matching extractions in the text of comments. Samha et al [10] use WordNet words (http://wordnet.princeton.edu) for explicit extraction, by querying the names of related fields in the dictionary. Synonym information was used to identify explicit aspects of Zhu et al [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Attribute-Sentiment Pair Correlation Model Based on Online User Reviews

Chen

et al. 2019

Journal of Sensors

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With the popularization of Internet applications and the rapid development of e-commerce, online shopping has become a widespread and important pattern of consumption. Online user comments are an important data asset on e-commerce sites and have a great potential value for online users and merchants. However, accurate and effective extraction of the characteristics of products and users’ sentiment evaluation from a tremendous amount of comments is a significant challenge. Based on the concept of the LinLog energy model, this paper proposes an online review attribute-sentiment pair correlation model that evaluates user comments. After preprocessing the comment data of mobile phones and constructing an attribute dictionary, the proposed model conducts a clustering analysis of attributes and sentiment pairs to gain accurate assessment of attributes in order to explore potential information from user comments. Experiments conducted on one real-world dataset with comprehensive measurements verify the efficacy of the proposed model.

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Apect-Based Opinion Extraction From Customer Reviews

Cited by 29 publications

References 13 publications

Helpfulness of product reviews as a function of discrete positive and negative emotions

Helpfulness of product reviews as a function of discrete positive and negative emotions

Enactment of Ensemble Learning for Review Spam Detection on Selected Features

Attribute-Sentiment Pair Correlation Model Based on Online User Reviews

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