Reviews sentiment analysis based on deep learning in social media

• Main Author: Zhu, Jiahao
• Other Authors: Mao Kezhi
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Computer and Information Science; Social media; Deep learning; Sentiment analysis
• Online Access: https://hdl.handle.net/10356/175943
• Institution: Nanyang Technological University

With the rise of social media, the number of social media users around the world has been growing and the way people communicate information has been revolutionised. Social media play an increasingly important role in the process of social information dissemination and interaction. Sentiment analysis, as a process of identifying, extracting and inferring the author's emotional tendencies from texts, has important applications in social media. Traditional methods have some challenges when dealing with social media texts, therefore, this study aims to improve the accuracy and efficiency of social media sentiment analysis using deep learning techniques. Specific objectives include proposing a text preprocessing method based on social media comments, comparing different word embedding techniques, and adapting the deep learning model structure to improve the accuracy and usefulness of sentiment analysis. This study provides new ideas and methods for the application of deep learning in the field of sentiment analysis, and promotes the further development and application of sentiment analysis techniques in social media and other fields.