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Mining of multi-media data and Clustering of Online user-generated content for Business Analytics

Madhusmita Behera, Aniket Kumar Pandey, Hritik Ranjan Sharma, Ishaan Kumar, K Aruna Bai

Abstract


As businesses recognize the significance of social networks for growth, understanding consumer sentiments be- comes vital. This project delves into the world of social media’s data analysis to gain actionable insights. It focuses on a popular brand and utilizes social media platform’s APIs to collect and assess customer consents. The TF-IDF algorithm is employed for vectorization, along with VADER, to determine the senti- ment—whether it’s positive, negative, or neutral. Additionally, a k-means technique is utilized to cluster the data with similar words, aiming to discover certain business value. Through this study, we explore both the technical and business aspects of text and media content on social media data, offering insights on its potential and future prospects. This paper explores the technical and business aspects and mining of Digital content on social media applications presenting potential future opportunities in this evolving field.


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References


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