Chatbot Emotion Recognition & Music Recommendation

Abstract

Emotion popularity and music suggestion through chatbots make use of deep learning methodologies to improve human-computer interplay thru textual content, vocalization, and facial expressions.  This look at gives a multimodal technique that carries Convolutional Neural Networks (CNN), long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), and LSTM-GRU networks, alongside BERT embeddings, to identify emotions throughout various input modalities.  The Kaggle Emotion Dataset is employed for training and contains tagged textual statistics that indicates diverse emotional states.  Preprocessing methods embody text sanitization and numerical vectorization making use of BERT for characteristic extraction.  The assessment of version overall performance is performed by way of accuracy, precision, recall, and F-score measurements.  The BiLSTM version attains a most accuracy of 91.84%, illustrating its exceptional functionality in spotting sequential dependencies and contextual subtleties in textual input.  A web-primarily based framework is established to permit real-time emotion recognition, allowing customers to have interaction with the chatbot through text enter, facial expressions captured with the aid of a webcam, or voice recordings.  A customized music advice system provides tracks that correspond to the user's emotional state based at the diagnosed emotion.  This approach improves human-computer connection by means of providing a sensible and responsive emotion-sensitive system.