Deep learning technologies have significantly advanced the field of text mining by enhancing the capability to process, analyze, and extract meaningful information from vast amounts of unstructured text data. Key technologies include Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for capturing
sequential dependencies in text, Convolutional Neural Networks (CNNs) for text classification, and attention mechanisms and Transformers like BERT and GPT for parallel processing and understanding context. Word embeddings (e.g., Word2Vec, GloVe) provide semantic representations of words, while sequence-to-sequence models enable applications such as text summarization and machine translation. Additionally, self-supervised and zero-shot learning broaden the adaptability of models across various text mining tasks. These technologies drive applications like sentiment analysis, entity recognition, document summarization.