Research on Feature Learning Algorithm of Social Media based on Deep Network

• Published in: 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC) pp. 1 - 5
• Main Authors: Bai, Haoming, Wu, Linjiao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04.12.2023
• Subjects: Blogs; Context modeling; Deep learning; learning algorithm; Representation learning; Semantics; social media features; Social networking (online); Task analysis
• DOI: 10.1109/ICMNWC60182.2023.10435995

Deep learning is a branch of machine learning, which has been used to solve many problems in the fields of computer vision, speech recognition, natural language processing and so on. Deep neural networks are trained by inputting large amounts of data into them, and then they learn how to recognize patterns from these data. This process is called supervised training because the network learns from tagged examples (such as images) or other inputs (such as audio files or text documents). The social media based performed by two models such as Deep network structure feature learning and Feature learning of social media based on deep network. The suggested approach achieved the better transmission of social media by M10, DBLP and Cora of values about 0.70, 0.75 and 0.80 respectively. The problem with deep learning is that it is difficult to train these networks on new tasks without accessing a large amount of tag data for each task. In addition, there are many challenges in teaching the knowledge learned in the learning process.