https://github.com/salman-khan-mohammed/tweets-topic-modeling-with-lda-and-interactive-word-cloud-visualization
This project focuses on analyzing tweets from Twitter using topic modeling techniques and interactive visualizations. It employs Latent Dirichlet Allocation (LDA) to discover topics within the tweet data and generates interactive word clouds based on topic-term strengths derived from the model. Users can explore topics and related tweets interactiv
https://github.com/salman-khan-mohammed/tweets-topic-modeling-with-lda-and-interactive-word-cloud-visualization
data-collection lda ntscraper scraping topic-modelling tweets-extraction twitter wordcloud-visualization
Last synced: 2 months ago
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This project focuses on analyzing tweets from Twitter using topic modeling techniques and interactive visualizations. It employs Latent Dirichlet Allocation (LDA) to discover topics within the tweet data and generates interactive word clouds based on topic-term strengths derived from the model. Users can explore topics and related tweets interactiv
- Host: GitHub
- URL: https://github.com/salman-khan-mohammed/tweets-topic-modeling-with-lda-and-interactive-word-cloud-visualization
- Owner: Salman-Khan-Mohammed
- Created: 2024-06-15T06:31:36.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-06-21T10:31:39.000Z (12 months ago)
- Last Synced: 2025-01-31T00:19:38.888Z (4 months ago)
- Topics: data-collection, lda, ntscraper, scraping, topic-modelling, tweets-extraction, twitter, wordcloud-visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 10.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Tweets-Topic-Modeling-with-LDA-and-Interactive-Word-Cloud-Visualization
This project focuses on analyzing tweets from Twitter using topic modeling techniques and interactive visualizations. It employs Latent Dirichlet Allocation (LDA) to discover topics within the tweet data and generates interactive word clouds based on topic-term strengths derived from the model. Users can explore topics and related tweets interactively, similar to tools like Tweet Topic Explorer. The project includes data collection, cleaning, LDA modeling, visualization, and a report showcasing top topics for a selected Twitter handler.