Stress Sentiment Detection Model This project implements a Stress Sentiment Detection Model designed to classify text into various stress sentiment categories. Using a dataset of textual entries labeled for stress levels, the model applies machine learning techniques to determine the sentiment and potential stress markers in text data.

Key Features: Data Cleaning & Preprocessing: Text data is preprocessed to remove unnecessary elements such as links, stopwords, punctuation, and special characters, enhancing the accuracy of feature extraction.

Sentiment Analysis: Sentiment scores for polarity and subjectivity are calculated using TextBlob, providing additional insights into emotional tone and intensity.

Feature Extraction with TF-IDF: Text data is transformed into numerical form using TF-IDF, capturing the importance of words relative to their frequency across the dataset.

Machine Learning Models: Multiple models, including Naive Bayes, Logistic Regression, Decision Tree, Random Forest, Support Vector Classifier, and Bernoulli Naive Bayes, are trained and evaluated. Each model provides insights into accuracy, precision, and F1-score.

Evaluation Metrics: Confusion matrix and classification reports offer a detailed look at model performance, highlighting strengths in classifying stress sentiments effectively.

This model can be applied in various domains, such as mental health monitoring and customer support analysis, to understand stress levels and underlying sentiment patterns in textual data.