Text Keyword Extraction Based on GPT

This study investigates text keyword and phrase extraction methods based on the GPT-3.5 model,and validates their effectiveness through comparative analysis. Initially, researchers employ the GPT-3.5 model for extracting keywords and phrases from text to uncover crucial information within the text. Subsequently, the extracted data from GPT-3.5 is compared with the key text from the original dataset to assess extraction performance and consistency. Lastly, extracted keywords are utilized for sentiment analysis, conducting comparative experiments with the BERT-TextCNN model, and validation across diverse datasets. The research findings demonstrate the GPT-3.5 model's capability to efficiently extract crucial textual information and significantly enhance sentiment classification precision. This enhancement contributes to improved performance and interpretability in text analysis, thereby providing substantial support for the field of natural language processing.