Fine-grained sentiment analysis for social media: from multi-model collaboration to cross-language multimodal analysis
Year: 2025
Type: article
Abstract: With the rapid development and widespread popularity of the Internet, the amount of data in social media and networks is growing exponentially, and sentiment analysis for this huge amount of data is very complex but significant. Fine-grained sentiment analysis has become the choice of researchers when dealing with various sentiment analysis tasks. Different from coarse-grained sentiment analysis, which only focuses on emotional polarity, fine-grained sentiment analysis involves emotional polarity and emotional intensity and recipients, providing more specific information about emotions. This paper aims to provide relevant research methods on fine-grained sentiment analysis and apply them to social network texts to analyze the challenges and solutions. This paper will classify fine-grained sentiment analysis from three methods: rule-based, machine learning and deep learning. This research finds that fine-grained sentiment analysis can not only accurately capture the emotions in the text, but also judge the direction and intensity of emotions, and understand different types of emotions in the text more specifically. This is of great help in dealing with more complex texts, such as social network texts. Combining fine-grained sentiment analysis with various large models can solve many challenges and problems when dealing with social network texts.
Author: Yuanmiao Dong
Institution: College of International Business and Economics, WTU
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411041707