基于不同模型的推特仇恨言论检测

Anagha Abraham, Antony J Kolanchery, Anugraha Antoo Kanjookaran, Binil Tom Jose, Dhanya Pm
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引用次数: 0

摘要

Twitter的主要目标是促进自由表达和思想交流,允许个人不受任何限制或约束地与他人分享他们的想法、观点和信息。它帮助人们感知不同的范围和观点。它是用来为公众讨论服务的,不应该因为种族、国籍、公共地位、等级、性取向、年龄、残疾或健康状况而被用来诋毁个人。因此,使用仇恨言论是不合适的,消除仇恨言论是实现目标的必要条件。本文旨在利用逻辑回归、支持向量机、随机森林、CNN-LSTM和模糊方法等机器学习算法来比较和评估它们在检测仇恨言论方面的准确性。目标是确定仇恨言论检测的最佳模型。
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Hate Speech Detection in Twitter Using Different Models
Twitter’s primary objective is to facilitate free expression and the exchange of ideas, allowing individuals to share their thoughts, opinions, and information with others without any limitations or constraints. It helps a human being to perceive different scopes and points of view. It is used to serve the public discussion and it should not be used to undermine individuals based on their race, nationality, public standing, rank, sexual orientation, age, disability, or health conditions. So, using hate speech is not appropriate and removal of hate speech is necessary for achieving the goal. This paper aims to utilize machine learning algorithms such as Logistic Regression, Support Vector Machine, Random Forest, CNN-LSTM, and Fuzzy method to compare and evaluate their accuracy in detecting hate speech. The objective is to determine the best model for hate speech detection.
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