A Novel Hybrid Model Based on Sentiment Analysis Using Community Guided Link Prediction for Connecting People With Similar Interest

• Published in: IEEE access Vol. 12; pp. 139435 - 139455
• Main Authors: Luthra, Snigdha, Kumar, Rakesh, Gupta, Meenu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Analytical models; Blogs; Bullying; Cluster analysis; Clustering; Clustering algorithms; Cohesion; Community detection; Cyberbullying; Data mining; Detection algorithms; Digital media; link prediction; Modularity; Multilayers; Network analysis; Nodes; Performance evaluation; Prediction algorithms; Qualitative analysis; Semantics; Sentiment analysis; sentimental analysis; social media networks (SMNs); Social networks; User experience; User interfaces
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3435142

In this study, a novel hybrid recommendation model is designed for popular social media platforms such as Facebook, Twitter, WhatsApp, and Instagram. Recognizing the global significance of these platforms, the model aims to facilitate connections among users with similar interests, leveraging community detection algorithms. By integrating sentimental analysis, link prediction, and community detection techniques, a comprehensive network is constructed that enhances user experience and addresses the escalating issue of cyberbullying in the era of increasing connectivity. The proposed model strategically combines nodes and edges to form communities tailored to individual user interests. Social media comments, gathered randomly from the Facebook API, undergo sentimental analysis to evaluate the model's performance. The innovative Community-guided Link Prediction algorithm using Sentimental Analysis (CLPSA) for multilayer networks is employed, aiding in the identification of nodes associated with fake comments and thereby mitigating cyberbullying problems. Integrating sentiment analysis enhances understanding of human emotions, and users in the same cluster are more likely to connect in the future, promoting positive engagement. The model's novelty lies in its utilization of the Proposed Cluster Edge Betweenness algorithm, which identifies clusters of users with denser connections among them while being loosely connected to other separate modules. This approach helps create a network structure that fosters positive interactions and reduces the occurrence of intolerable comments. The experimental comparison of community detection algorithms underscores the effectiveness of our approach, particularly the superior performance of the Cluster Edge Betweenness algorithm. It achieves high accuracy in identifying user interests and forming cohesive clusters, essential for enhancing user engagement and community cohesion within social media networks. The qualitative analysis of comments leads to the formation of communities within the network, uniting users with similar interests into cohesive clusters. The uniqueness of the approach lies in the integration of sentimental analysis, link prediction, and community detection algorithms within a multilayer network framework. Specifically, the proposed Cluster Edge Betweenness algorithm is compared with two other community detection algorithms (Cluster Label Proportionate and ClusterFastGreedy). Results demonstrate the superior accuracy of the algorithm, achieving a 95% accuracy in aligning with user interests and a notably high modularity density of 0.2331, indicative of superior clustering quality. By incorporating these elements, the model can evolve into a more comprehensive solution for addressing cyberbullying and promoting positive engagement in social media networks.