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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Enhanced link prediction using sentiment attribute and community detection.

Debadatta Naik1, Dharavath Ramesh1, Naveen Babu Gorojanam1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004 India.

Journal of Ambient Intelligence and Humanized Computing
|January 2, 2023
PubMed
Summary
This summary is machine-generated.

Link prediction in social networks can be improved by considering user sentiment and community structure. This approach enhances accuracy by analyzing shared emotions and group affiliations, moving beyond just network topology.

Keywords:
Community detectionLink predictionSVMSentiment analysis

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Area of Science:

  • Social Network Analysis
  • Computational Social Science

Background:

  • Link prediction traditionally relies on network topology, often overlooking user engagement and discussion topics.
  • Existing methods may not accurately capture relationship formation based on shared interests or sentiments.

Purpose of the Study:

  • To propose a novel link prediction algorithm integrating user sentiment and community structure with topological features.
  • To enhance the accuracy of predicting future user connections in social networks.

Main Methods:

  • Developed a new algorithm combining network topology, user sentiment analysis, and community detection.
  • Collected and analyzed COVID-19 related tweets from various countries on Twitter for evaluation.

Main Results:

  • Experimental results demonstrate that shared emotions and community affiliations significantly influence user connections.
  • The proposed method shows improved performance in link prediction compared to topology-only approaches.

Conclusions:

  • Incorporating sentiment and community structure is crucial for more accurate social network link prediction.
  • This approach offers a more nuanced understanding of user relationship dynamics beyond simple network structures.