Hate speech recognition in multilingual text: hinglish documents

The Internet is a boon for mankind but its misuse has been increasing drastically. Social networking platforms such as Facebook, Twitter and Instagram play a predominant role in expressing views by the users. Sometimes users wield abusive or inflammatory language, that may provoke readers. This pape...

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Bibliographic Details
Published in:International journal of information technology (Singapore. Online) Vol. 15; no. 3; pp. 1319 - 1331
Main Authors: Yadav, Arun Kumar, Kumar, Mohit, Kumar, Abhishek, Shivani, Kusum, Yadav, Divakar
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.03.2023
Springer Nature B.V
Subjects:
Accuracy
Artificial Intelligence
Computer Imaging
Computer Science
Datasets
Deep learning
Digital media
Embedding
Hate speech
Image Processing and Computer Vision
Internet
Literature reviews
Machine Learning
Misogyny
Original Research
Pattern Recognition and Graphics
Social networks
Software Engineering
Speech recognition
Violence
Vision
Words (language)
India
Deep learning
CNN
BiLSTM
Hate speech
Machine learning
Word2Vec
ISSN:2511-2104, 2511-2112
Online Access:Get full text
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Summary:The Internet is a boon for mankind but its misuse has been increasing drastically. Social networking platforms such as Facebook, Twitter and Instagram play a predominant role in expressing views by the users. Sometimes users wield abusive or inflammatory language, that may provoke readers. This paper aims to evaluate various machine learning and deep learning techniques to detect hate speech on various social media platforms in the Hinglish (English-Hindi code-mix) language. In this paper, we apply and evaluate several machine learning and deep learning methods, along with various feature extraction and word-embedding techniques, on a consolidated dataset of 20600 instances, for hate speech detection from tweets and comments in Hinglish. The experimental results reveal that deep learning models perform better than machine learning models in general. Among the deep learning models, the CNN-BiLSTM model with word2vec word embedding provides the best results. The model yields 0.876 accuracy, 0.830 precision, 0.840 recall and 0.835 F1-score. These results surpass the recent state-of-art approaches.
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ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-023-01211-z