https://github.com/yaminibhole/social_media_sentiment_analysis
Social Media Sentiment Analysis: Analyze and visualize sentiment patterns in social media data to gain insights into public opinion on specific topics or brands. This project uses Python scripts to uncover sentiments, offering bar plots for sentiment distribution and word clouds for visual representation.
https://github.com/yaminibhole/social_media_sentiment_analysis
python sentiment-analysis textblob-sentiment-analysis wordcloud-visualization
Last synced: 2 months ago
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Social Media Sentiment Analysis: Analyze and visualize sentiment patterns in social media data to gain insights into public opinion on specific topics or brands. This project uses Python scripts to uncover sentiments, offering bar plots for sentiment distribution and word clouds for visual representation.
- Host: GitHub
- URL: https://github.com/yaminibhole/social_media_sentiment_analysis
- Owner: yaminibhole
- Created: 2023-12-11T06:39:28.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-16T13:04:02.000Z (over 1 year ago)
- Last Synced: 2025-02-01T22:28:24.669Z (4 months ago)
- Topics: python, sentiment-analysis, textblob-sentiment-analysis, wordcloud-visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 301 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Social Media Sentiment Analysis
This repository contains code for analyzing and visualizing sentiment patterns in social media data. The purpose of the project is to gain insights into public opinion and attitudes towards specific topics or brands based on social media content.
# Requirements
1. Python 3.x
2. pandas
3. matplotlib
4. seaborn
5. textblob
6. wordcloud
This GitHub repository encompasses a sentiment analysis and visualization project tailored for social media data. By utilizing Python scripts, the project aims to uncover public sentiments and attitudes towards particular topics or brands. Users can clone the repository, install the required dependencies, and execute the provided code on their own datasets. The output includes a bar plot showcasing sentiment distribution and a word cloud for a visual representation of the textual content.