TITLE:
User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity
AUTHORS:
Yuting Tan, Vijay K. Madisetti
KEYWORDS:
Social Media, User Behavior Analysis, Sentiment Analysis, Data Mining, Machine Learning, User Profiling, Cybersecurity, Behavioral Insights, Personality Prediction
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.17 No.6,
June
7,
2024
ABSTRACT: As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solution’s technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.