Utilizing GPT and Deep Learning Networks for Image Analysis of Apple News Headlines
Yiping Jiang, Guanpeng Su · 2024
This study explores the fusion of Generative Pretrained Transformers (GPT) and Attention-based Long Short-Term Memory (Attention-based LSTM) models within deep learning networks to analyze concurrent Apple news headlines. The research delves into the collaboration between sophisticated natural language processing techniques, such as GPT, and convolutional or recurrent neural networks, aimed at extracting meaningful features from textual data. Through the meticulous evaluation of performance metrics and comparative analyses, the effectiveness of this integrated approach is firmly established. The findings offer valuable insights for the improvement of multimedia content analysis and recommendation systems on digital news platforms, thus contributing significantly to the evolution of sentiment analysis within the domain of Apple-related news.