https://github.com/axestein/twitter-sentimental-analysis-using-ml

https://github.com/axestein/twitter-sentimental-analysis-using-ml

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• Host: GitHub
• URL: https://github.com/axestein/twitter-sentimental-analysis-using-ml
• Owner: Axestein
• Created: 2024-01-25T16:28:07.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-01-25T16:34:57.000Z (11 months ago)
• Last Synced: 2024-01-25T17:44:52.248Z (11 months ago)
• Language: Jupyter Notebook
• Size: 16.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Twitter-Sentimental-analysis-using-ml

Project description

This repository contains the code and resources for a project that analyzes the sentiment of tweets on a specific topic using the Twitter API and Google Colab. The project utilizes various libraries and techniques to achieve this goal, including:

Twitter API: Accesses real-time tweet data on the chosen topic.

NLTK and VADER: Perform sentiment analysis on the extracted tweets.

Data Preprocessing: Cleans and prepares the tweet text for analysis.

Visualization: Displays the sentiment distribution and insights using charts and graphs.

Data Source:

This project utilizes Twitter data accessed through the Twitter API and obtained from a relevant Kaggle dataset. Please refer to the data folder for details on the specific dataset used.

Technical Stack:

Language: Python

Libraries: NLTK, VADER, Tweepy, Matplotlib, Pandas, etc.

Environment: Google Colab

Getting Started:

Install Required Libraries:

Run the following command in your terminal within the project directory:

Bash

pip install -r requirements.txt

Use code with caution. Learn more

Configure Twitter API Keys:

Create a Twitter developer account and obtain your API keys (Consumer Key, Consumer Secret, Access Token, Access Token Secret). Update the credentials.py file with your retrieved keys.

Run the Sentiment Analysis Script:

Run the sentiment_analysis.py script in your Google Colab environment. This script will collect tweets, perform sentiment analysis, and generate visualizations.

Explore the Results:

The outputs of the script will be displayed in Google Colab, including sentiment scores, distributions, and visualizations. Feel free to modify the script and explore different topics or parameters to further analyze the collected tweets.

File Structure:

data: Folder containing the used Twitter data (if applicable).

scripts: Folder containing the Python scripts for data processing, sentiment analysis, and visualization.

credentials.py: File containing your Twitter API keys.

requirements.txt: File containing the list of required libraries.

README.md: This file (the one you're reading now).

Further Work:

Expand the analysis to consider different sentiment categories (e.g., positive, negative, neutral).

Implement topic modeling to identify different themes within the tweets.

Compare the sentiments across different groups of users or time periods.

Develop a real-time sentiment analysis dashboard to monitor ongoing Twitter trends.

Contributions:

This project welcomes contributions and suggestions. Feel free to fork the repository, make changes, and submit pull requests with your improvements.