![](/images/graphics-bg.png)
Opinion-based co-occurrence network for identifying the most influential product features
Joint Authors
Source
Journal of Engineering Research
Issue
Vol. 8, Issue 4 (31 Dec. 2020), pp.185-205, 21 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2020-12-31
Country of Publication
Kuwait
No. of Pages
21
Main Subjects
Information Technology and Computer Science
Abstract EN
Nowadays, social networking sites such as Facebook, Twitter, LinkedIn, YouTube, and other e-commerce websites produce a large number of text reviews.
These text reviews mostly describe the product features and their opinions, which are the most important to the product developers, launchers, or buyers for business development and decisionmaking processes.
Therefore, we present an opinion-based co-occurrence network for product reviews.
The main aim of this research is to identify the popularity of product features or popular terms, the number of connections of a term, the strong relationship between terms, grouping the product terms, and the sentiment polarity links between terms in both positive sentiment and negative sentiment.
Also, we employed the Harel-Koren fast multiscale layout algorithm and CNM (Clauset-Newman-Moore) algorithm for visualizing and grouping the network.
We then measured the overall graph metrics and vertex metrics to characterize the network.
Additionally, the experimental result shows the ranked product features and their social strength between product features and sentiments.
American Psychological Association (APA)
Kumar, Ashok J.& Abirami, S.. 2020. Opinion-based co-occurrence network for identifying the most influential product features. Journal of Engineering Research،Vol. 8, no. 4, pp.185-205.
https://search.emarefa.net/detail/BIM-1494675
Modern Language Association (MLA)
Kumar, Ashok J.& Abirami, S.. Opinion-based co-occurrence network for identifying the most influential product features. Journal of Engineering Research Vol. 8, no. 4 (Dec. 2020), pp.185-205.
https://search.emarefa.net/detail/BIM-1494675
American Medical Association (AMA)
Kumar, Ashok J.& Abirami, S.. Opinion-based co-occurrence network for identifying the most influential product features. Journal of Engineering Research. 2020. Vol. 8, no. 4, pp.185-205.
https://search.emarefa.net/detail/BIM-1494675
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 202-205
Record ID
BIM-1494675