Social Media Sentiment Analysis System

Overview

Social media platforms generate a massive amount of textual data with diverse sentiments. This project aims to develop a sentiment analysis system capable of automatically classifying social media posts into positive, negative, or neutral sentiments. The system leverages advanced natural language processing (NLP) techniques and machine learning algorithms to achieve high accuracy in sentiment classification.

Features

• Data collection from various social media platforms (Twitter, Reddit, etc.)
• Text preprocessing including tokenization, stop-word removal, stemming/lemmatization, and handling hashtags, emojis, and slang
• Feature extraction using bag-of-words, TF-IDF, word embeddings, and contextual embeddings
• Model training and evaluation using machine learning and deep learning algorithms
• User-friendly interface for real-time sentiment analysis

Installation

Prerequisites

• Python 3.9 or higher
• pyenv for managing Python versions
• pyenv-virtualenv for managing virtual environments

Setup

1. Clone the repository
2. Set up the environment using Python's built-in venv:

python -m venv venv
source venv/bin/activate  # Mac/Linux
venv\Scripts\activate  # Windows
pip install -r requirements.txt

Usage

Data Collection

• Gather a dataset of social media posts using APIs or web scraping techniques.
• Annotate the dataset with sentiment labels using pretrained models from Hugging Face.

Preprocessing

• Perform text preprocessing steps such as tokenization, stop-word removal, stemming/lemmatization, and lowercasing.
• Handle specific challenges of social media text like hashtags, emojis, and slang.

Feature Extraction

• Experiment with different feature representation methods such as bag-of-words, TF-IDF, word embeddings (e.g., Word2Vec or GloVe), or contextual embeddings (e.g., BERT or GPT).

Model Training and Evaluation

• Choose a suitable machine learning algorithm (e.g., Naive Bayes, SVM, or neural networks) or deep learning model for sentiment classification.
• Split the dataset into training and testing sets.
• Train the selected model using the training data, evaluate and record its performance on the training and testing data.

Deployment and Interface

• Develop a simple user-friendly interface using any library (Tkinter is simple one for instance) that allows users to input social media posts and obtain sentiment analysis results in real-time.
• Display the sentiment analysis result on the interface and allow users to input additional posts.

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature-branch).
6. Open a pull request.

Contact

For any questions or feedback, feel free to reach out to me at [email protected]