NLP • Machine Learning • Data Analytics
HealthInsight AI
Healthcare NLP & Sentiment Analysis
A healthcare text analytics application for sentiment analysis, model evaluation, pattern visualization, and analytical reporting.
- Python
- Streamlit
- NLP
- scikit-learn
- TF-IDF
- Plotly
Project overview
An AI-powered healthcare text analytics dashboard designed to analyze patient and healthcare feedback using natural language processing and machine learning.
The application transforms unstructured textual feedback into sentiment analysis, NLP exploration, model evaluation, visual analytics, and downloadable reports.
A text analytics and sentiment analysis application — not a medical diagnosis system.
Key features
- CSV and Excel dataset upload
- Single-text sentiment analysis
- Bulk sentiment analysis
- Text preprocessing pipeline
- Lowercasing
- HTML, URL, and email removal
- Contraction expansion
- Tokenization
- Stopword removal while preserving negation
- Lemmatization
- N-gram exploration
- TF-IDF and Bag-of-Words analysis
- Logistic Regression
- Multinomial Naive Bayes
- Support Vector Machine
- Model comparison using macro F1
- Confusion matrix
- ROC analysis
- Feature importance
- Word clouds
- Analytical insights
- Misclassified example analysis
- PDF, CSV, and Excel reporting
Implementation / workflow
- Upload feedback or enter a single text, then preprocess text while preserving negation.
- Represent text with TF-IDF or Bag of Words and compare classification models using macro F1.
- Inspect confusion matrices, ROC analysis, and misclassified examples; export PDF, CSV, or Excel reports.