Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2023

Abstract

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.

Keywords

video sentiment analysis, text analysis, facial expression analysis, audio analysis, child safety

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Information Systems and Management

Publication

2023 International Conference on Data Mining, ICDM: Shanghai, December 1-4: Proceedings

First Page

783

Last Page

790

ISBN

9798350381641

Identifier

10.1109/ICDMW60847.2023.00106

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICDMW60847.2023.00106

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