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Human emotions heavily influence our life decisions and to be able to detect them takes things on another level. The proposed solution builds upon this by utilizing a complete guide on how different machine learning, deep learning, and ensemble models behave on different types of datasets including textual and visual data consisting of images. In our approach, the effectiveness of the ensemble model is studied, a comparison between CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM is classified, and the behavior of Naive Bayes, KNN, decision tree, random forest, and SVM models is also analyzed. Through this comparative analysis, the research aims in finding the best model that helps in identifying and predicting emotions accurately. This research shows that the ensemble model works the best to accurately detect emotion present within the data.

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Emotion Detection—A Comparative Analysis Using Machine Learning and Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Yashika Baranwal,
  • Shreya Gupta,
  • Kanika Singla,
  • Ankita

摘要

Human emotions heavily influence our life decisions and to be able to detect them takes things on another level. The proposed solution builds upon this by utilizing a complete guide on how different machine learning, deep learning, and ensemble models behave on different types of datasets including textual and visual data consisting of images. In our approach, the effectiveness of the ensemble model is studied, a comparison between CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM is classified, and the behavior of Naive Bayes, KNN, decision tree, random forest, and SVM models is also analyzed. Through this comparative analysis, the research aims in finding the best model that helps in identifying and predicting emotions accurately. This research shows that the ensemble model works the best to accurately detect emotion present within the data.