Harnessing the Power of GPT-3 and LSTM for Natural Language Processing in Agricultural Product News: Focus on Soybeans
Zijun Liang, Meiying Cui, Rouying Wu, Xianze Ye, Guanpeng Su · 2023
In recent years, there have been remarkable advancements in deep learning and natural language processing (NLP) techniques, leading to significant improvements in extracting valuable insights from textual data. However, applying these techniques effectively to domain-specific content, such as agricultural news, presents unique challenges. In this study, we investigate the utilization of GPT-3 (Generative Pre-trained Transformer 3)’s impressive language understanding capabilities in conjunction with LSTM (Long Short-Term Memory) networks, which are well-known for their ability to capture sequential dependencies. Our objective is to enhance the processing and analysis of soybean-related news headlines. Through our experimental analysis, we demonstrate that the fusion of GPT-3 and LSTM models results in a substantial improvement in the accuracy and effectiveness of sentiment analysis specifically in the context of agricultural product news. This research contributes to the expansion of knowledge and paves the way for advancements in the field of emotional analysis within the context of soybean-related news.