TEst Project 16 Jan 26

This project focuses on developing an AI-based system that automatically analyzes customer feedback and determines the overall sentiment expressed in the text. The system uses Machine Learning and Natural Language Processing (NLP) techniques to classify customer reviews as positive, negative, or neutral.

The model is trained on a labeled dataset of customer reviews. Text preprocessing steps such as tokenization, stop-word removal, and stemming are applied to improve accuracy. Features are extracted using techniques like TF-IDF, and a supervised learning algorithm (such as Logistic Regression or Support Vector Machine) is used for classification.

The system helps businesses understand customer opinions, identify common issues, and improve their products or services based on data-driven insights.

Key Features

• Automated sentiment classification
• Text preprocessing and feature extraction
• Machine learning model training and evaluation
• Visualization of sentiment distribution
• Scalable for large volumes of feedback

Technologies Used

• Python
• Scikit-learn
• Natural Language Processing (NLP)
• Pandas & NumPy
• Matplotlib / Seaborn

Applications

• Customer feedback analysis
• Product review monitoring
• Brand reputation management