This project aims to classify text data into sentiment categories (positive or negative) and categorical classes using machine learning models. The project includes both sentiment analysis and multi-class classification tasks.
The dataset used in this project consists of labeled text samples, where each sample is associated with a sentiment label (positive or negative) and a category label (e.g., electronics, books, clothing, grocery, patio).
Data Directory from where the datasets are created: Here
For sentiment analysis, we utilize supervised learning techniques to train models that classify text into positive or negative sentiment categories. We explore various machine learning algorithms such as Support Vector Machines (SVM), Logistic Regression, and Naive Bayes.
For category classification, we train models to classify text samples into one of the predefined categories. We experiment with different classification algorithms and feature engineering techniques to achieve optimal performance.
sentiment_classifier.ipynb: Jupyter notebook containing the sentiment analysis code.category_classifier.ipynb: Jupyter notebook containing the category classification code.