Abstract
We develop and implement a framework and a decision algorithm to determine the best feature extraction technique (FET) for supporting machine learning-based hate speech detection. Specifically, the contributions of this work are three-fold: (1) a seamless modular pipeline that automatically preprocesses, vectorizes, and classifies whether or not a text message is a hate speech; (2) a decision algorithm that determines the best FET approach among all the possible FET candidates with the linear time complexity O(N); and (3) a preliminary experimental evaluation on the tweets provided by Twitter Sentiment Analysis on Analytics Vidhya to demonstrate that our FET framework and decision algorithm are effective and produce the significant results.
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Ngan, CK., Bhuva, K. (2021). A Framework and Decision Algorithm to Determine the Best Feature Extraction Technique for Supporting Machine Learning-Based Hate Speech Detection. In: Lee, R. (eds) Computer and Information Science 2021—Summer . ICIS 2021. Studies in Computational Intelligence, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-79474-3_2
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