Twitter Sentimental Analysis

Twitter Sentimental Analysis is a Python-based project aimed at analyzing the sentiment of tweets. The goal is to classify tweets as positive, negative, or neutral, providing insights into public opinion on various topics. This project uses data science and machine learning techniques to process and analyze Twitter data effectively.

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Project Overview

Social media platforms like Twitter generate vast amounts of sentiment-rich data. This project focuses on:

• Extracting data from Twitter using the Twitter Developer API.
• Preprocessing and cleaning the data.
• Analyzing the sentiment of tweets using machine learning models.
• Visualizing the results through plots and word clouds.

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Tools and Technologies

• Language Used: Python
• Libraries Used:
  • Tweepy for accessing the Twitter API.
  • TextBlob for sentiment analysis.
  • WordCloud for data visualization.
  • Pandas and NumPy for data manipulation and analysis.
  • Matplotlib for visualizations.
• Platform: Jupyter Notebook
• Operating System: Windows 11

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Key Features

1. Tweet Extraction:
   • Extracts tweets using Twitter Developer API with Tweepy.
   • Handles authentication and API keys.
2. Data Preprocessing:
   • Removes hyperlinks, special characters, and other irrelevant content.
   • Cleans and organizes data for analysis.
3. Sentiment Analysis:
   • Classifies tweets into positive, negative, and neutral categories.
   • Uses TextBlob for sentiment scoring.
4. Data Visualization:
   • Generates word clouds for frequent words.
   • Plots sentiment distributions and insights.
5. Model Evaluation:
   • Implements machine learning algorithms for better classification accuracy.

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Project Modules

1. Import necessary libraries and dependencies.
2. Extract and clean Twitter data.
3. Perform exploratory data analysis.
4. Visualize the data using charts and word clouds.
5. Split data into training and testing subsets.
6. Evaluate the model's performance with metrics.

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How to Run

1. Clone this repository:

   git clone https://github.com/rahul-as-rockey/Twitter_Sentimental_Analysis.git

2. Install the required libraries:

   pip install tweepy pandas numpy matplotlib textblob wordcloud

3. Run the Jupyter Notebook:

   jupyter notebook Twitter_SentiMental_Analysis.ipynb

4. Configure the Twitter Developer API keys in the code.

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Contributors

• Rahul Reddy Chidipudi

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References

• Twitter Developer API
• TextBlob Documentation
• Tweepy Documentation

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License

This project is licensed under the MIT License. See the LICENSE file for details.