In this project, we implement an end-to-end deep learning pipeline for predicting emojis based on textual input, showcasing key techniques in natural language processing (NLP) and sequence modeling. The system leverages pre-trained word embeddings (GloVe) and a Long Short-Term Memory (LSTM) network to capture the semantic relationships between words and map them to corresponding emoji labels.
This project demonstrates the practical application of machine learning techniques in solving NLP problems, emphasizing:
- Text Vectorization:
- Pre-trained embeddings convert text into numerical form, effectively encoding semantic meaning.
- Sequence Modeling with LSTM:
- LSTMs handle sequential data, capturing dependencies between words in a sentence.
- Multi-class Classification:
- Softmax activation and categorical cross-entropy loss enable accurate mapping of textual data to emoji classes.
- Word Embeddings:
- GloVe embeddings integrate contextual understanding from a large corpus, enhancing the model's semantic capabilities.
- LSTM Architecture:
- Designed with two stacked LSTM layers and dropout regularization for effective sequence processing and overfitting prevention.
- Performance Evaluation:
- Employs train-validation split and testing for robust evaluation.
- Visualization:
- Predicts emojis for given text inputs, comparing true and predicted results using visual cues.
This project simulates real-world tasks such as:
- Emoji suggestion in messaging apps.
- Context-aware emoji recommendations in social media platforms.
- Sentiment or tone analysis using emoji labels.
By presenting this project, candidates can showcase proficiency in text preprocessing, sequence modeling, and deep learning model implementation, making it an ideal demonstration for a machine learning interview.