🧠 Sentiment Analysis of Court Case Evidence

Sentiment Analysis of Court Case Evidence is an academic project that focuses on classifying the sentiment of legal text extracted from court case documents. The project aims to support legal decision-making by automatically analysing whether the extracted evidence expresses a positive, negative, or neutral sentiment.

📌 Project Type

🧪 Academic / NLP + Machine Learning

🎯 Objective

To build a system that takes court case textual evidence as input and predicts its sentiment using NLP (Natural Language Processing) and ML classification models, assisting in faster legal case studies and analysis.

🔍 How It Works

User provides text / statement extracted from court case records

Text is preprocessed (tokenization, stop-word removal, stemming/lemmatization)

Features are extracted using TF-IDF

A trained ML model predicts sentiment (Positive / Neutral / Negative)

Output is returned to the user

Layer	Technologies
Programming	Python
Libraries	NumPy, Pandas, Scikit-Learn, NLTK
ML	Logistic Regression / SVM (Model trained on prepared dataset)
Web App (optional)	Flask
Deployment	Local execution / Web UI

🚀 Features

✔ Takes raw legal text as input ✔ NLP preprocessing pipeline for legal documents ✔ Sentiment class prediction (Positive / Neutral / Negative) ✔ Works for real-world court evidence ✔ Can be integrated into case-analysis tools