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Leveraging AI Technologies for Public Sentiment and Trend Prediction on Social Media: A Deep Dive into Sentiment Analysis, Topic Modeling, and Graph Neural Networks
- 1 University of New South Wales, Sydney, Australia
* Author to whom correspondence should be addressed.
Abstract
With the rapid development of social media platforms, public opinion analysis and trend forecasting have become key decision-making capabilities for governments and enterprises. In this study, a real-time public opinion monitoring system is built by integrating multimodal AI technology, in which the convolutional neural network is responsible for emotion classification, the topic model mines hot events, and the graph neural network tracks the propagation path. The experiment captured data from Twitter and other platforms, adopted an efficient preprocessing process and feature extraction method, and confirmed that the accuracy rate of the CNN model in the emotion determination task reached 92.5%, which was significantly improved compared to traditional methods. In the case database, typical events such as the fluctuation of public opinion during the US election and the release of new energy vehicle products were successfully identified, and the GNN model effectively predicted the diffusion trajectory of related topics. This technological integration system provides accurate data for analyzing business competition and formulating public policies.
Keywords
Sentiment analysis, social media, deep learning, topic modeling, graph neural networks
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Cite this article
Cheng,X. (2025). Leveraging AI Technologies for Public Sentiment and Trend Prediction on Social Media: A Deep Dive into Sentiment Analysis, Topic Modeling, and Graph Neural Networks. Applied and Computational Engineering,151,51-56.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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