PredictA - Election Analyzer

PredictA_ElectionAnalyzer is a Python-based project designed to perform sentiment analysis on political tweets. It extracts and analyzes Twitter data to determine the favorability of political individuals or organizations, with the goal of providing insights to improve campaigning strategies.

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🎯 Core Purpose

The main objective of this project is to:

1. Extract Data: Gather political-related tweets from users on Twitter.
2. Analyze Sentiment: Process the collected tweets to understand public sentiment.
3. Classify & Predict: Use the Naïve Bayes algorithm to classify the sentiment and calculate the probability of how much a political entity is favored by the public.

The resulting analysis can be used to inform and refine political campaigning strategies.

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🛠️ Technology Stack

• Primary Language: Python
• Core Technique: Sentiment Analysis
• Classification Algorithm: Naïve Bayes

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📂 Repository Structure

The project is organized into several key modules:

• /DataExtraction: Scripts and modules responsible for fetching data (e.g., tweets).
• /Datapreprocessing: Code for cleaning and preparing the raw data for analysis.
• /Datamodification: Modules for transforming or modifying data structures.
• /DataAnalysing: The core logic for performing sentiment analysis and classification.
• /results: Directory where output reports or analysis results are stored.
• /M_PredictA_120619.pdf: Project report or documentation file.

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🚀 How to Use

While specific setup instructions are not detailed, the general workflow to use this project would be:

1. Clone the Repository:

   git clone https://github.com/maheshmnair/PredictA_ElectionAnalyzer.git

2. Install Dependencies: You will likely need to install Python libraries. (A requirements.txt file would typically list these, but you may need to inspect the import statements in the .py files). Common libraries for such a project include pandas, nltk, scikit-learn, and a Twitter API client like tweepy.

3. Configure API Keys: To use the /DataExtraction module, you will need to provide your own Twitter API credentials.

4. Run the Pipeline: Execute the Python scripts in the following general order:

   1. Run data extraction scripts.
   2. Run data preprocessing scripts.
   3. Run the data analysis and classification scripts.

5. Check the Results: Analyzed data and probabilities will be available in the /results directory.

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🤝 Contributing

Contributions, issues, and feature requests are welcome. Feel free to open an issue to discuss your ideas or report a bug.