Abstract
With recent development in digital technologies, the amount of multimedia statistics is increasing everyday. Abusive video constitutes a hazard to public safety and thus constructive detection algorithms are in urgent need. In order to improve the detection accuracy here, Sentiment analysis-based video classification is proposed. Sentiment analysis-based video classification system is used to classify video content into two different categories, i.e., Abusive videos, nonabusive videos. We are using YouTube comments of a video as source of input, which is analyzed by our sentiment analysis model and the model determines the category to which that particular video belongs. Many techniques such as Bag of Words, Lemmatization, logistic regression and NLP are used. The proposed scheme obtains competitive results on abusive content detection. The empirical outcome shows that our method is elementary and productive.
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Swain, D., Verma, M., Phadke, S., Mantri, S., Kulkarni, A. (2021). Video Categorization Based on Sentiment Analysis of YouTube Comments. In: Swain, D., Pattnaik, P.K., Athawale, T. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1311. Springer, Singapore. https://doi.org/10.1007/978-981-33-4859-2_6
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DOI: https://doi.org/10.1007/978-981-33-4859-2_6
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