A complete lifecycle of our Emotion Intelligence engine.
A labeled corpus containing thousands of text samples across seven emotion classes is ingested. Exploratory analysis is performed to understand class distribution, sentence length, and imbalance. The dataset is split into 80% Training and 20% Testing sets.
Raw text is normalized by removing punctuation, digits, and noise using Regex.
The Tokenizer maps words into integer sequences.
Sequences are padded to a fixed length (40 tokens) for batch consistency.
An Embedding layer converts tokens into dense vectors. These are passed through a Bidirectional LSTM layer to capture both past and future context. Dropout regularization reduces overfitting before the Softmax output layer.
The model is trained using categorical cross-entropy loss and Adam optimizer. Validation data monitors generalization. Early stopping and checkpointing preserve the best-performing weights.
Performance is measured using accuracy, precision, recall, and confusion matrix. Hyperparameters such as embedding size, LSTM units, and dropout rate are tuned to maximize classification reliability.
The optimized model (emotion_model.h5) is embedded into a Flask backend.
Incoming user text undergoes identical preprocessing before inference,
enabling low-latency, real-time emotion prediction in the web interface.