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social-media-analytics

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Taking a look at data of 1.6 million twitter users and drawing useful insights while exploring interesting patterns visualized with concise plots. The techniques used include text mining, sentimental analysis, probability, time series analysis and Hierarchical clustering on text/words using R.

  • Updated May 11, 2022
  • RMarkdown

machine learning project designed to analyze Instagram comments for sentiment detection, question identification, and topic modeling. Utilizing algorithms such as LDA, LSA, NMF, and BERT, CommentAnalyzer provides valuable insights into user interactions, helping brands and researchers understand audience sentiments and trends.

  • Updated Nov 1, 2024
  • Jupyter Notebook

This project focuses on extracting and analyzing social media data from Reddit to uncover meaningful insights . The goal is to help marketing analysts understand trending topics, audience sentiment, and engagement patterns. By examining these insights, marketers can make data-driven decisions to enhance campaign strategies and improve engagement.

  • Updated Nov 14, 2024
  • Jupyter Notebook

This project aims to analyze the influencer journey of Robin Sharma, focusing on his social media activities across YouTube, LinkedIn, and Instagram. By extracting data using the YouTube API and manually collecting data from LinkedIn and Instagram, we provide a detailed analysis of his posts, engagement, and costs.

  • Updated Jul 22, 2024
  • Jupyter Notebook

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