Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text

Abstract

The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data.

Best Fit DNA-Based Cryptographic Keys: The Genetic Algorithm Approach

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“…Hate speech is one of the most commonly trolling techniques that are used for cyberbullying. This kind of aggression-when it happens-in which a social media platform such as Twitter becomes a complex phenomenon, and many collaborative research fields are trying to solve this critical problem [6]. In the literature, many other works also have tried to address these issues, such as cyberbullying [6,7], trolling [8], extremism [9], hate speech [10,11] and racism [12,13].…”

“…This kind of aggression-when it happens-in which a social media platform such as Twitter becomes a complex phenomenon, and many collaborative research fields are trying to solve this critical problem [6]. In the literature, many other works also have tried to address these issues, such as cyberbullying [6,7], trolling [8], extremism [9], hate speech [10,11] and racism [12,13]. There are different approaches that could be applicable for feature engineering, including N-gram to generate the vectors from dictionary of hate-related words [14][15][16].…”

“…Twitter is a defendable and legitimate source of data for analysing the hateful content, and therefore, it is important to find the sentiments of the tweets and to finally detect them in an automatic fashion by proposing machine-learning techniques. Researchers have found promising outcomes for classifying hate speech from the textual information in terms of tweets [6].…”