Leveraging Data Mining to Analyze User Behavior in Social Networks
Session
Computer Science and Communication Engineering
Description
This thesis focuses on the development of an innovative platform for exploring topics on Twitter and extracting information about users and their interconnections. The platform leverages modern technologies such as Flask, Python, NLTK, HTML, CSS, and Docker to collect and analyze tweets, performing sentiment analysis on the content. The system generates interactive visual reports that highlight trends and relationships among users, providing a comprehensive overview of specific topics and their perception on social media. This project enables in-depth analysis and knowledge enrichment of selected themes through digital examination of tweets, offering users unique insights to inform decision-making and engagement strategies. By combining sentiment evaluation and user interaction mapping, the platform serves as a valuable tool for marketing, social support initiatives, and other applications that require understanding of public opinion and online discourse.
Keywords:
Data Mining, Social Networks, Sentiment Analysis, Community Detection, Behavior Prediction, Data Privacy, Artificial Intelligence
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-982-41-2
Location
UBT Lipjan, Kosovo
Start Date
25-10-2025 9:00 AM
End Date
26-10-2025 6:00 PM
DOI
10.33107/ubt-ic.2025.107
Recommended Citation
Çunaj, Gentrit; Kabashi, Faton; Shkurti, Lamir; Sofiu, Vehebi; and Selimaj, Mirlinda, "Leveraging Data Mining to Analyze User Behavior in Social Networks" (2025). UBT International Conference. 39.
https://knowledgecenter.ubt-uni.net/conference/2025UBTIC/CS/39
Leveraging Data Mining to Analyze User Behavior in Social Networks
UBT Lipjan, Kosovo
This thesis focuses on the development of an innovative platform for exploring topics on Twitter and extracting information about users and their interconnections. The platform leverages modern technologies such as Flask, Python, NLTK, HTML, CSS, and Docker to collect and analyze tweets, performing sentiment analysis on the content. The system generates interactive visual reports that highlight trends and relationships among users, providing a comprehensive overview of specific topics and their perception on social media. This project enables in-depth analysis and knowledge enrichment of selected themes through digital examination of tweets, offering users unique insights to inform decision-making and engagement strategies. By combining sentiment evaluation and user interaction mapping, the platform serves as a valuable tool for marketing, social support initiatives, and other applications that require understanding of public opinion and online discourse.