Image Content Analysis for Social Media Public Opinion Monitoring and Response Strategies
Lina Lin, Dezhi Wei · Traitement du signal · 2024
With the widespread use of social media, the formation and dissemination speed of online public opinion has accelerated, and the influence of public opinion events has become increasingly significant.Traditional public opinion monitoring methods mainly rely on text analysis.However, in the context of social media, multimedia content such as images and videos has become an important carrier of public opinion dissemination.Images not only convey emotional information in a direct manner but also play a key role in public opinion events.Therefore, image-based public opinion monitoring has become a research hotspot and a challenge.Existing studies mainly focus on text analysis, with insufficient in-depth analysis of image content, and there are certain limitations in areas such as semantic understanding and sentiment orientation judgment.This paper aims to explore how to enhance the accuracy of social media public opinion monitoring and response strategies through image content analysis.Firstly, the paper analyzes the shortcomings of traditional public opinion monitoring methods in terms of semantic usage and proposes improvement ideas.Secondly, an image content analysis model for social media public opinion monitoring is constructed, using deep learning and other technologies to extract emotional and social inclination information from images.Finally, based on the results of image content analysis, response strategies for social media public opinion are proposed, providing theoretical support and practical guidance for public opinion management and crisis response.This study not only addresses the shortcomings of existing methods and improves the accuracy of public opinion monitoring but also provides feasible suggestions for responding to social media public opinion, offering significant application value.