Video sentiment analysis for child safety

• Main Authors: TAN, Yee Sen, TEO, Nicole Anne Huiying, GHE, Ezekiel En Zhe, FONG, Jolie Zhi Yi, WANG, Zhaoxia
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: video sentiment analysis; text analysis; facial expression analysis; audio analysis; child safety; Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/8356; https://ink.library.smu.edu.sg/context/sis_research/article/9359/viewcontent/Video_Sentiment_Analysis_for_Child_Safety_Final.pdf
• Institution: Singapore Management University

The proliferation of online video content underscores the critical need for effective sentiment analysis, particularly in safeguarding children from potentially harmful material. This research addresses this concern by presenting a multimodal analysis method for assessing video sentiment, categorizing it as either positive (child-friendly) or negative (potentially harmful). This method leverages three key components: text analysis, facial expression analysis, and audio analysis, including music mood analysis, resulting in a comprehensive sentiment assessment. Our evaluation results validate the effectiveness of this approach, making significant contributions to the field of video sentiment analysis and bolstering child safety measures. This research serves as a valuable resource for those seeking to employ sentiment analysis to protect children from harmful content within the dynamic landscape of video content. Furthermore, our work offers insights into the current state of the art, highlighting the recent advancements, possible improvements, and future directions in video sentiment analysis.