Fintell

FINTELL is an AI-powered financial sentiment analysis application designed to automatically classify customer reviews from financial institutions into positive, neutral, or negative sentiment categories.

The project aims to help banks and financial companies better understand customer feedback at scale, identify recurring issues, monitor customer satisfaction, and support data-driven decision making. Instead of manually reviewing thousands of comments, users can leverage machine learning models to obtain fast and consistent sentiment predictions.

The project was built using Python and a complete machine learning workflow. The dataset was cleaned and preprocessed using NLP techniques such as text normalization and TF-IDF vectorization. Several machine learning models were evaluated, including Naive Bayes, Logistic Regression, Support Vector Machines, and ensemble methods. Model performance was assessed using classification metrics such as precision, recall, F1-score, and macro F1-score. The application was developed following best practices, including modular code organization, pipelines, model persistence, and reproducible experiments.

Future improvements include fine-tuning Transformer-based models such as DistilBERT or RoBERTa, implementing explainable AI techniques to understand model predictions, deploying the application as a cloud service, and extending the platform with real-time sentiment monitoring and topic detection capabilities.