Cited by 152Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningText and Document Classification Technologies

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Detection of Hate Speech Text in Hindi-English Code-mixed Data

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Sreelakshmi, K, Premjith, B, Soman, K.P.·2020·Procedia Computer Science

DOI 10.1016/j.procs.2020.04.080

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Abstract

Social media sites like Twitter, Facebook, being user-friendly and a free source, provide opportunities to people to air their voice. People, irrespective of the age group, use these sites to share every moment of their life making these sites flooded with data. Apart from these commendable features, these sites have down side as well. Due to lack of restrictions set by these sites for its users to express their views as they like, anybody can make adverse and unrealistic comments in abusive language against anybody with an ulterior motive to tarnish one’s image and status in the society. So it became a huge responsibility for the Government and these sites to identify this hate content before it disseminates to mass. Automatic hate speech detection faces quite a lot of challenges due to the non-standard variations in spelling and grammar. Especially for a country like India with huge multilingual and bilingual population, this hate content would be in code-mixed form which makes the task demanding. So our paper projects a machine learning model to detect hate speech in Hindi-English code-mixed social media text. The methodology makes use of Facebook’s pre-trained word embedding library, fastText to represent 10000 data samples collected from different sources as hate and non-hate. The performance of the proposed methodology is compared with word2vec and doc2vec features and it is observed that fastText features gave better feature representation with Support Vector Machine (SVM)-Radial Basis Funcrion (RBF) classifier. The paper also provides an insight to the researchers working in the field of code-mixed data that character level features provide best result for code-mixed data.

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Authors: Sreelakshmi, K., Premjith, B., & Soman, K. P.
Affiliations: Amrita Vishwa Vidyapeetham (IN)
Corresponding author: Sreelakshmi, K
ORCID: Sreelakshmi, K
Premjith, B
Soman, K.P.
Venue: Procedia Computer Science
Publisher: Elsevier BV
Published: 2020
Volume: 171
Pages: 737-744
ISSN: 1877-0509 (ISSN-L)
DOI: 10.1016/j.procs.2020.04.080
OpenAlex: W3032985518
Cited by: 152
References: 16
Citations by year: 2026: 15 · 2025: 30 · 2024: 25 · 2023: 40 · 2022: 21 · 2021: 18 · 2020: 3
FWCI: 7.82
Type: conference paper
Language: en
Open access: diamond
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor, 107 d)
Locations: Procedia Computer Science (publishedVersion, cc-by-nc-nd)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Keywords: hate speech detection, Hindi-English code-mixing, social media text, FastText embeddings, word embeddings, character-level features, support vector machine, Gaussian kernel, multilingual text classification, abusive language

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Detection of Hate Speech Text in Hindi-English Code-mixed Data

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