Abstract
Aggression and hate speech is a rising concern in social media platforms. It is drawing significant attention in the research community who are investigating different methods to detect such content. Aggression, which can be expressed in many forms, is able to leave victims devastated and often scar them for life. Families and social media users prefer a safer platform to interact with each other. Which is why detection and prevention of aggression and hatred over internet is a must. In this paper we extract different features from our social media data and perform supervised learning methods to understand which model produces the best results. We also analyze the features to understand if there is any pattern involved in the features that associates to aggression in social media data. We used state-of-the-art cognitive feature to gain better insight in our dataset. We also employed ngrams sentiment and Part of speech features as a standard model to identify other hate speech and aggression in text. Our model was able to identify texts that contain aggression with an f-score of 0.67.
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Notes
- 1.
https://en.wikipedia.org/wiki/Aggression Date: 11/22/2018.
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Iqbal, S., Keshtkar, F. (2023). Using Cognitive Learning Method to Analyze Aggression in Social Media Text. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_15
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