Welcome to the Urdu Sentiment Analysis project repository! 🌟 Here, I've curated the code and resources from my comprehensive exploration into sentiment analysis in the Urdu language, employing a variety of deep learning models and word embeddings techniques.
- GRU LSTM: Dive into my implementation of Gated Recurrent Unit (GRU) Long Short-Term Memory (LSTM) networks for sentiment analysis in Urdu text.
- Bidirectional LSTM (BiLSTM): Explore my BiLSTM model architecture, capturing contextual information from both directions for enhanced sentiment understanding.
- RNN: Witness my Recurrent Neural Network (RNN) model in action, designed to analyze sentiment patterns in Urdu text.
- Word2Vec: Leveraging pre-trained Word2Vec embeddings and exploring techniques for efficient loading and embedding of Urdu text.
- GloVe: Uncover insights from my utilization of Global Vectors for Word Representation (GloVe) embeddings for sentiment analysis in Urdu.
- FastText: Delve into my implementation of FastText embeddings, enriching Urdu sentiment analysis with subword information.
- ELMo: Witness the power of context-aware embeddings with my implementation and training of ELMo embeddings for Urdu sentiment analysis.
My mission is to empower sentiment analysis in the Urdu language, facilitating better understanding and interpretation of text data. Join me as I explore the nuances of sentiment analysis and pave the way for advancements in natural language processing for Urdu.
- Data Mining Discoveries: Unearth insights driving innovation in sentiment analysis and data mining.
- Language Processing Connections: Forge links between sentiment analysis and real-world applications in language processing.
- Deep Learning Discoveries: Dive into the intersection of deep learning and sentiment analysis for groundbreaking solutions.
Join me in unraveling the complexities of sentiment analysis in Urdu text and shaping the future of natural language processing! 📊✨