This repository contains projects related to text classification and sentiment analysis using machine learning and deep learning techniques. It includes the following projects:
In this project, we implemented a Random Forest model to classify emails as spam or not spam (ham).
- Data Preparation: The dataset used for training and testing.
- Preprocessing: Steps taken to clean and prepare the text data.
- Feature Extraction: Methods used for converting text data into numerical features.
- Model Training: How the Random Forest model was trained.
- Evaluation: Evaluation metrics used to assess the model's performance.
This project focuses on text classification using Long Short-Term Memory (LSTM) networks, a type of deep learning model.
- Data Preparation: Details about the dataset used.
- Preprocessing: Text preprocessing steps applied to the data.
- Model Architecture: Information about the LSTM-based model.
- Training: How the model was trained.
- Evaluation: Metrics used to assess the model's performance.
This project performs sentiment analysis on Twitter data to understand the sentiment of text data.
- Data Collection: How Twitter data was collected.
- Preprocessing: Steps taken to clean and prepare the Twitter text data.
- Sentiment Analysis: Techniques used to determine sentiment (positive, negative, neutral).
- Results: Summary of sentiment analysis results.
- Instructions for setting up the necessary environment and dependencies for running the code.
- How to use the code and models in this repository for your own text classification or sentiment analysis tasks.
This project is licensed under the MIT License.
- If you have any questions or feedback, feel free to contact ganeshgorakhvanave@gmail.com.