This repository implements a system for analyzing the sentiment of tweets using natural language processing (NLP) and machine learning techniques. It aims to classify tweets as positive, negative, or neutral based on their textual content.
- Data Collection: Collects real-time tweets using the Twitter API or pre-existing datasets.(https://www.kaggle.com/datasets/kazanova/sentiment140)
- Data Preprocessing: Cleans and preprocesses the tweet text by removing noise, stopwords, and special characters.
- Sentiment Analysis: Classifies tweets into positive, negative, or neutral categories using machine learning models trained on labeled data.
- Visualization: Generates visualizations such as word clouds, sentiment distribution plots, and trend analysis graphs.
- Feature Engineering: Extracts relevant features from tweets (e.g., word frequencies, sentiment lexicons).
- Model Training: Trains various machine learning models (e.g., Naive Bayes, Logistic Regression, Support Vector Machines, Neural Networks) for sentiment classification.
- Visualization: Visualizes results and insights (e.g., sentiment distribution, word clouds).
- Model Evaluation: Evaluates the performance of the sentiment analysis models using metrics like accuracy, precision, recall, and F1-score.
- Deployment: Optionally, deploy the trained model as a web application or API for real-time sentiment analysis.
- Python
- Natural Language Processing (NLP) libraries (NLTK, SpaCy)
- Machine Learning libraries (Scikit-learn, TensorFlow, PyTorch)
- Twitter API (Tweepy)
- Web development frameworks (Flask, Django) for deployment (optional)
To get started with this project, follow these steps:
- Clone the repository to your local machine.
- Install the required dependencies using
pip install -r requirements.txt. - Collect or obtain Twitter data (either through the API or pre-existing datasets).
- Preprocess the data using the provided preprocessing scripts.
- Train and evaluate machine learning models for sentiment analysis.
- Visualize the results and insights gained from the analysis.
- Hafiz Ans
- Contributors are welcome to submit pull requests and suggest improvements.
Special thanks to the developers of the open-source libraries and datasets used in this project.
For inquiries or support, please contact (anssabrar11@gmail.com).