An exploratory experiment on Hindi, Bengali hate-speech detection and transfer learning using neural networks

Phung, Tung Minh; Cloos, Jan

doi:10.48550/arxiv.2201.01997

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…(5). According to the survey, there is an increase in the content of Indian language hostile posts on social media, like Hindi (1, 2, 6-9), Marathi (7,(10)(11)(12), Bengali (8), Khasi (9), Punjabi, Gujarati and English language (9). Very less research work has been done in the lowresource Indian languages (5).…”

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confidence: 99%

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Rameshbhai,

Rana

2024

IJE

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Hostile post on social media is a crucial issue for individuals, governments and organizations. There is a critical need for an automated system that can investigate and identify hostile posts from large-scale data. In India, Gujarati is the sixth most spoken language. In this work, we have constructed a major hostile post dataset in the Gujarati language. The data are collected from Twitter, Instagram and Facebook. Our dataset consists of 1,51,000 distinct comments having 10,000 manually annotated posts. These posts are labeled into the Hostile and Non-Hostile categories. We have used the dataset in two ways: (i) Original Gujarati Text Data and (ii) English data translated from Gujarati text. We have also checked the performance of pre-processing and without pre-processing data by removing extra symbols and substituting emoji descriptions in the text. We have conducted experiments using machine learning models based on supervised learning such as Support Vector Machine, Decision Tree, Random Forest, Gaussian Naive-Bayes, Logistic Regression, K-Nearest Neighbor and unsupervised learning based model such as k-means clustering. We have evaluated performance of these models for Bag-of-Words and TF-IDF feature extraction methods. It is observed that classification using TF-IDF features is efficient. Among these methods Logistic regression outperforms with an Accuracy of 0.68 and F1-score of 0.67. The purpose of this research is to create a benchmark dataset and provide baseline results for detecting hostile posts in Gujarati Language.

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Section: Introductionmentioning

confidence: 99%

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Rameshbhai,

Rana

2024

IJE

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“…According to the survey, there is an increase in the content of Indian language hostile posts on social media. The hostile post are identified in Indian languages like Hindi [2,3,[7][8][9][10], Marathi [8] [11][12][13], Bengali [9], Punjabi, Gujarati in addition to the English language [10]. Very less research work has been done in the low-resource Indian languages [6].…”

Section: Introductionmentioning

confidence: 99%

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Boda

2023

Preprint

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Hostile post on social media is a crucial issue for individual, government, and organizations. There is a critical need for an automated system that can investigate and identify hostile posts from large-scale data. In India, Gujarati is the sixth most spoken language. In this work, we have constructed a major hostile post dataset in the Gujarati language. The data are collected from Twitter, Instagram, and Facebook. Our dataset consists of 1,51,000 distinct tweets having 10,000 manually annotated posts. These posts are labeled into the Hostile and Non-Hostile categories. We have used the dataset in two ways: (i) Original Gujarati Text Data and (ii) Gujarati Data translated into English. We have also checked the performance of pre-processing and without pre-processing data by removing extra symbols and substituting emoji descriptions in the text. Finally, we have conducted experiments using machine learning models based on supervised learning such as Support Vector Machine, Decision Tree, Random Forest, Gaussian Naive-Bayes, Logistic Regression, K-Nearest Neighbor and unsupervised learning based model such as k-means clustering. We have evaluated performance of these models for Bag-of-Words and TF-IDF feature extraction methods. It is observed that classification using TF-IDF features is efficient. Among these methods Logistic regression outperforms with an Accuracy of 0.68 and F1-score of 0.67.

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An exploratory experiment on Hindi, Bengali hate-speech detection and transfer learning using neural networks

Cited by 2 publications

References 13 publications

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

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