This project implements a Sentiment Analysis Chatbot that classifies text input as positive or negative sentiment using machine learning. The chatbot features a graphical user interface (GUI) built with Python's tkinter library, making it easy for users to interact with the model in real-time.
The primary objective of this project is to develop a sentiment analysis model that can accurately classify text data (e.g., user reviews, comments) as having a positive or negative sentiment. This model is then integrated into a chatbot interface to provide real-time sentiment analysis feedback to the user.
- Machine Learning Model: Utilizes a Logistic Regression model trained on a dataset of product reviews to classify sentiment.
- Graphical User Interface: The chatbot is built with
tkinter, providing a user-friendly interface. - Real-Time Analysis: Users can input text and instantly receive sentiment feedback.
- Modular Design: The project is organized to easily swap in different models or datasets.
The sentiment analysis model was trained on a dataset of Amazon product reviews. The dataset includes text reviews labeled as either positive or negative based on their star rating.
- Data Preprocessing: The text data was cleaned by removing HTML tags, special characters, and stopwords, and then vectorized using
TfidfVectorizer. - Model Training: A Logistic Regression model was trained on the preprocessed data, and an alternative SVM model was also tested.
- Python 3.x
- Required libraries:
scikit-learn,tkinter,pickle