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Sentiment Analysis on Online Videos by Time-Sync Comments.

Jiangfeng Li1, Ziyu Li1, Xiaofeng Ma2

  • 1School of Software Engineering, Tongji University, Shanghai 201804, China.

Entropy (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a sentiment analysis model to automatically identify video highlights using time-synchronized user comments. This approach enhances highlight detection efficiency for video editors.

Keywords:
sentiment analysissentimental intensity calculationtime-sync commentsvideo highlight extraction

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Manual video highlight editing is time-consuming and inefficient.
  • Audience engagement with video highlights relies on identifying interesting or meaningful shots.
  • Automating highlight detection is crucial for improving video production workflows.

Purpose of the Study:

  • To develop an automated system for recognizing sentiments in video highlights.
  • To assist video editors in locating and extracting meaningful video content more efficiently.
  • To quantitatively assess the sentimental intensity of video shots.

Main Methods:

  • A sentiment analysis model was designed to process time-synchronized user comments.
  • The model analyzes time-series comment data to detect sentiment information.
  • A sentimental intensity calculation method was developed to quantify shot sentiments.

Main Results:

  • The proposed approach significantly improves the F1 score by 12.8% in sentiment extraction.
  • An 8.0% increase in overlapped number was observed compared to existing methods.
  • The model successfully extracts sentiments and sentimental intensities of video highlights.

Conclusions:

  • The developed sentiment analysis model offers an efficient solution for automatic video highlight detection.
  • This method assists video editors by streamlining the process of identifying engaging content.
  • The quantitative sentiment analysis provides valuable insights for video content evaluation.