🌟 Assamese Sentiment Analysis with LSTM

A deep learning-powered tool to classify Assamese text as Positive, Negative, or Neutral using an LSTM model tailored for the Assamese language.

🚀 Key Features

• 🔍 Sentiment Analysis for Assamese – Supports full sentiment classification of Assamese text
• 🧠 Deep Learning Backbone – Powered by TensorFlow/Keras with a Long Short-Term Memory (LSTM) network
• ✨ Advanced Preprocessing – Includes tokenization, text cleaning, optional stemming, and stopword removal
• 🧰 Custom Tokenization – Leverages AssameseTokenizer for accurate language handling
• 📈 Robust Evaluation Metrics – F1-score, precision, recall, and accuracy

🧠 Model Overview

🧪 Installation & Requirements

⚙️ Model Description

• 📚 Training Data The dataset was curated from public sources such as news articles, social media comments, and feedback forms, and was manually labeled into three sentiment classes: Positive, Neutral, and Negative.
• 🏋️ Training Procedure
• ✂️ Preprocessing: Text cleaning, tokenization using AssameseTokenizer, optional stemming and stopword removal
• 🔢 Input Handling: Sequences padded or truncated to a fixed length of 512 tokens
• 🧠 Architecture: Embedding layer → LSTM → Dense (Softmax)
• 💧 Regularization: Dropout layers to prevent overfitting
• ⚙️ Optimizer: Adam
• 🔁 Epochs: Trained for X epochs (replace with your actual number)
• 📊 Evaluation: Final validation accuracy and F1-score: Insert actual metrics here

📦 Intended Usage

• 🗨️ Social media sentiment tracking in Assamese
• 📢 Public opinion & brand monitoring
• 📚 Research on low-resource NLP in Indic languages
• ⚠️ Limitations / Not Recommended For:

🧪 Quickstart: Using the Model

📚 Reference Citations

• E. Grave*, P. Bojanowski*, P. Gupta, A. Joulin, T. Mikolov, Learning Word Vectors for 157 Languages
• Assamese Tokenizer

🤝 In Collaboration with

• Angshita Kashyap
• Dhiraj Ballav Saikia
• Pratyushee Talukdar