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The Social Media Platform YouTube was launched in 2005, and now it is a powerful platform for education, entertainment, and much more. In the education industry, Online Education has achieved significant progress post-COVID. Nowadays, YouTube is playing a vital role in the education sector. Many free educational content has made YouTube famous among learners of different levels. These learners post various comments below the video about their emotions about the video. These comments are the basis for new viewers and allow new users to determine whether watching the video will be fruitful. Hence, it becomes crucial to analyze the sentiment of these comments to aid the content creator in knowing about the potential and weaknesses of the content. This study uses the advanced machine learning algorithm NLP (Natural Language Processing) to assess the emotions from the comments section. The study uses Python’s powerful Natural Language Toolkit (NLTK) and its SentimentIntensityAnalyzer class to carry out the study. We analyzed 27,850 comments in total from five different domains. Our findings are as follows: Educational content (15,920 comments) showed 48.7% positive, 9.0% negative, and 42.3% neutral sentiments; Entertainment videos (6,289 comments) exhibited 22.0% positive, 1.9% negative, and 76.1% neutral; Spiritual content (357 comments) had 35.9% positive, 7.0% negative, and 57.1% neutral; Travel vlogs (3,929 comments) demonstrated 59.1% positive, 10.5% negative, and 30.4% neutral sentiments; Science videos (1,355 comments) revealed 26.6% positive, 16.6% negative, and 56.8% neutral. These results aid content creators in optimizing video quality based on viewer engagement and sentiment.

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Understanding Viewer Emotions Across Diverse Content Domains Using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Anand Kumar,
  • Rashmi Agrawal

摘要

The Social Media Platform YouTube was launched in 2005, and now it is a powerful platform for education, entertainment, and much more. In the education industry, Online Education has achieved significant progress post-COVID. Nowadays, YouTube is playing a vital role in the education sector. Many free educational content has made YouTube famous among learners of different levels. These learners post various comments below the video about their emotions about the video. These comments are the basis for new viewers and allow new users to determine whether watching the video will be fruitful. Hence, it becomes crucial to analyze the sentiment of these comments to aid the content creator in knowing about the potential and weaknesses of the content. This study uses the advanced machine learning algorithm NLP (Natural Language Processing) to assess the emotions from the comments section. The study uses Python’s powerful Natural Language Toolkit (NLTK) and its SentimentIntensityAnalyzer class to carry out the study. We analyzed 27,850 comments in total from five different domains. Our findings are as follows: Educational content (15,920 comments) showed 48.7% positive, 9.0% negative, and 42.3% neutral sentiments; Entertainment videos (6,289 comments) exhibited 22.0% positive, 1.9% negative, and 76.1% neutral; Spiritual content (357 comments) had 35.9% positive, 7.0% negative, and 57.1% neutral; Travel vlogs (3,929 comments) demonstrated 59.1% positive, 10.5% negative, and 30.4% neutral sentiments; Science videos (1,355 comments) revealed 26.6% positive, 16.6% negative, and 56.8% neutral. These results aid content creators in optimizing video quality based on viewer engagement and sentiment.