eddiegulay/Swahili-Sement-Classification

This project includes sentiment analysis using deep learning with TensorFlow framework, written in Python. It is licensed under the MIT license. # Swahili Sentiment Classifier This project focuses on sentiment classification, which involves predicting the sentiment or emotion associated with a given text. The goal is to train a machine learning model to accurately classify text into positive, negative, or neutral sentiments. ## Project Overview The sentiment classification project consists of the following major steps: 1. **Data Preprocessing:** - Loading and inspecting the dataset - Cleaning the text by removing special characters, punctuation, URLs, and HTML tags - Tokenizing the text and converting it to sequences - Padding the sequences to ensure uniform length - Splitting the dataset into training and testing sets - One-hot encoding the sentiment labels 2. **Model Creation:** - Designing and building a deep learning model using a sequential architecture - Adding layers such as embedding, LSTM, and dense layers to the model - Compiling the model with appropriate loss function, optimizer, and metrics 3. **Model Training:** - Training the model on the preprocessed training data - Monitoring the training progress and optimizing hyperparameters - Evaluating the model's performance on the validation set, if applicable 4. **Model Evaluation:** - Evaluating the model's performance on the testing set - Calculating relevant evaluation metrics such as accuracy, precision, recall, and F1-score - Analyzing the results and gaining insights into the model's strengths and weaknesses ## Usage To use this project: 1. Clone the repository: ```bash git clone github.com ``` 2. Install the required dependencies: ```bash pip install -r requirements.txt ``` 3. Run the preprocessing script to clean and preprocess the text data: ```bash python preprocessing.py ``` 4. Run the training script to train the sentiment classification model: ```bash python train.py ``` Model training accur …