Text Emotion Multi-Label Classification with Vectoring and Deep Learning

An important problem in natural language processing is text emotion multi-label classification, which seeks to predict various emotions connected to a given text. This research suggests a unique method for increasing the precision of text emotion classification that combines vectorization methods and deep learning models. Then, using methods like TF-IDF or word embeddings, the text input is preprocessed and converted into numerical vectors. Then, a deep learning architecture with numerous layers, like CNNs or RNNs, is used to input vectorized data. Accurate emotion classification is made possible by the deep learning model, which efficiently learns the many correlations and patterns found in the text data. The effectiveness of the suggested strategy in achieving high classification accuracy for numerous emotions is demonstrated by experimental findings on a benchmark dataset.