Research on news headlines classification based on long short term model-text recurrent neural network

In the traditional Natural Language Process (NLP) neighborhood, there is a work that must be discussed called "text classification", which is widely used in sentiment analysis, news recommendation, and spam detection. The development of text classification techniques has evolved from manual rules and math statistical methods to machine learning and deep learning. With advancements in self-supervised learning and multimodal learning, text classification is becoming more intelligent and efficient. This study focuses on exploring the application performance of the TextRNN model in news headline classification. An experiment conducted on the public dataset show that TextRNN performs excellently in multiple evaluation metrics. The model effectively captures the semantics and contextual information in news headlines, making it particularly suitable for handling short-text news headline data. This paper provides a detailed experimental design and conducts an in-depth analysis of different parameter settings and model optimization strategies to ensure the scientific validity and reliability of the experimental results. Based on the trained TextRNN model, it can quickly and accurately predict the classification of user-input news texts, resulting in a text classification model applicable to real-world scenarios.