SMART PUBLIC OPINION MONITORING AND ANALYSIS SYSTEM FOR ETHNIC REGIONS: A MULTI-MODAL APPROACH USING MACHINE LEARNING

ABSTRACT

In ethnic regions, public opinion plays a vital role in shaping social stability and harmony. However, monitoring and analyzing public opinion in these regions can be challenging due to the complex linguistic and cultural factors involved. To address this issue, this study proposes a Smart Public Opinion Monitoring and Analysis System for Ethnic Regions, which utilizes a multi-modal approach for data acquisition and machine learning techniques for analysis. The system collects public opinion data from multiple modalities, including text, speech, image, and video, and uses intelligent speech processing and text translation to analyze non-textual data. The sentiment analysis module evaluates the polarity of public opinion, while the topic modeling module identifies key topics and keywords of public interest. The system also includes an early warning and risk detection module that uses machine learning algorithms to detect potential risks and generate alerts in real-time. Finally, the data visualization module presents the analysis results in an intuitive and user-friendly manner. The proposed system has been tested on a dataset of public opinion data from ethnic regions, and the results demonstrate its effectiveness in monitoring and analyzing public opinion in different modalities. The system can help government agencies and relevant stakeholders to respond quickly to potential risks and maintain social stability in ethnic regions.

KEYWORDS

Public Sopinion Analysis, Multi-Modal Data Acquisition, Machine Learning, Intelligent Speech Processing, Data Visualization, Sentiment Analysis