Opinion-based co-occurrence network for identifying the most influential product features


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 decision-making 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 multi-scale 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.

Author Biographies

Ashok Kumar Jayaraman, Anna University, Chennai, India
Ashok Kumar J is a research scholar with the Department of Information Science and Technology, Anna University, Chennai. He received a Bachelor of Science in Mathematics from University of Madras and Master of Computer Applications from Anna University. His research interests include natural language processing, sentiment analysis, machine learning, and social network analysis.
Abirami S, Anna University, Chennai, India

Abirami S is an assistant professor with the Department of Information Science and Technology, Anna University, Chennai. She received her M.E. and a Ph.D. degree in Computer Science and Engineering. She received research projects from the University grants commission (UGC), Government of India and center for technology development and transfer, Anna University. She also received a Junior Research Fellowship, CTS Best Young Faculty Award and Mentor Award from CTS and Center for Technology Development Transfer, Anna University respectively. She has contributed research papers in refereed journals and conferences. Her research interests include video analytics, text mining, artificial intelligence, programming languages, and big data analytics.


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Computer Engineering