Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

Ali, Muhammad Zain; Ehsan-ul-Haq,; Rauf, Sahar; Javed, Kashif; Hussain, Sarmad

doi:10.1109/access.2021.3087827

Cited by 32 publications

(12 citation statements)

References 31 publications

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“…Numerous research investigations have meticulously delved into the realm of identifying hate speech, with a notable emphasis on discerning hate speech within political contexts, as is evident from the works of (Oriola & Kotze, 2020; Ribeiro et al, 2018; Wang et al, 2022). Additionally, the academic discourse extends to the scrutiny of hate speech intertwined with religious themes, as underscored by (Al‐Hassan & Al‐Dossari, 2022; Ali et al, 2021; Ghosh et al, 2023; Mozafari et al, 2022; Qureshi & Sabih, 2021; Sajid et al, 2020). Visual representation of the distribution of these topics is offered in Figure 12 and Table 9, which convey the number of instances within each thematic domain.…”

Section: Discussionmentioning

confidence: 99%

“…Race (Mathew et al, n.d.;Alatawi et al, 2021;Chiu et al, 2021;MacAvaney et al, 2019;Sajid et al, 2020;Shannaq et al, 2022;Sharma & Shrivastava, 2018;Zhang & Luo, 2018) Religion (Sajid et al, 2020;Zhang & Luo, 2018), (Mathew et al, n.d.;Ali et al, 2021;Mozafari et al, 2022;Qureshi & Sabih, 2021) Ethnicity (Ali et al, 2021;Ghosh et al, 2023;Mozafari et al, 2022;Pronoza et al, 2021;Watanabe et al, 2018) Sexist (Al-Hassan & Al-Dossari, 2022;Baydogan & Alatas, 2021;Chiu et al, 2021;MacAvaney et al, 2019;Mozafari et al, 2022;Qureshi & Sabih, 2021;Shannaq et al, 2022) Political (Govers et al, 2023;Oriola & Kotze, 2020;Plaza-Del-Arco et al, 2021;Ribeiro et al, 2017;Wang et al, 2022) Women and immigrants (Irani et al, 2021;MacAvaney et al, 2019;Zhou et al, 2020) Covid-19 (Ahmed & Lin, 2022;Khanday et al, 2022;Su et al, 2023) Gender (Ghosh et al, 2023) Crime (Mathew et al, n.d.) and those addressing hate speech within the framework of the COVID-19 outbreak, such as Khanday et al (2022) and…”

Section: Target Studymentioning

confidence: 99%

“…Traditional machine learning SVM (Ali et al, 2021;Balouchzahi et al, 2021;Oriola & Kotze, 2020;Saeed Tawfik, 2020;Sajid et al, 2020;Sharma & Shrivastava, 2018;Vogel & Meghana, 2021;Watanabe et al, 2018) NB (Priyadarshini et al, 2023;Saeed Tawfik, 2020;Sajid et al, 2020;Sharma & Shrivastava, 2018) RF (MacAvaney et al, 2019;Oriola & Kotze, 2020;Saeed Tawfik, 2020;Sharma & Shrivastava, 2018) LR (Balouchzahi et al, 2021;Oriola & Kotze, 2020;Saeed Tawfik, 2020;Sajid et al, 2020) GB (Oriola & Kotze, 2020;Sajid et al, 2020) DT (Priyadarshini et al, 2023) NLP (Das et al, 2021;Saeed Tawfik, 2020;Sharma & Shrivastava, 2018;Vogel & Meghana, 2021;Watanabe et al, 2018) K-means (Ali et al, 2021) Word2vec (Balouchzahi et al, 2021;Priyadarshini et al, 2023) GloVe (Priyadarshini et al, 2023;Rodriguez et al, 2022) Deep learning CNN (Bilal et al, 2022;Das et al, 2021;Ghosh et al, 2023;Zhang & Luo, 2018;Zhou et al, 2020) LSTM (Bilal et al, 2022) BERT (Alzahrani & Jololian, 2021;…”

Section: Category Technique Studymentioning

confidence: 99%

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Hate speech detection in social media: Techniques, recent trends, and future challenges

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

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The realm of Natural Language Processing and Text Mining has seen a surge in interest from researchers in hate speech detection, leading to an increase in related studies. This analysis aims to create a valuable resource by summarizing the methods and strategies used to combat hate speech in social media. We perform a detailed review to achieve a deep knowledge of the hate speech detection landscape from 2018 to 2023, revealing global incidents of hate speech in 2022–2023. Sixty‐six relevant articles were selected for this review. Existing studies were analyzed and categorized into five method categories: Machine Learning, Deep Learning, Ensemble models, Graph Neural Networks, and Graph Convolutional Networks. These advancements can aid social networking services in identifying hate messages before being posted, reducing the risk of harassment. The review also covers available hate speech datasets and highlights research challenges, but it is clear that a definitive solution to this problem is yet to be found. Future research directions are recommended to address the ongoing challenges in Hate Speech Detection.This article is categorized under: Applications of Computational Statistics > Computational Linguistics Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Classification and Regression Trees (CART) Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining

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Section: Discussionmentioning

confidence: 99%

Section: Target Studymentioning

confidence: 99%

Section: Category Technique Studymentioning

confidence: 99%

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Hate speech detection in social media: Techniques, recent trends, and future challenges

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

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“…Offensive texts, hate speech, and misogynous language are often correlated with negative sentiments, whereas the tone, context, and content is often highly loaded with polarized language (Ali et al, 2021;Gitari et al, 2015); as such, our intuition to enhance the UNITER model with a sentiment classifier. We focused only on image modality since large language models often already capture features required for text sentiment analysis.…”

Section: Visual Sentiment-enhanced Unitermentioning

confidence: 99%

UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification

Paraschiv¹,

Dascălu²,

Cercel³

2022

Preprint

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In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hatespeech detection, has not received the same attention. In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI -Multimedia Automatic Misogyny Identification. The shared task aimed to identify misogynous content in a multi-modal setting by analysing meme images together with their textual captions. To this end, we propose two models based on the pre-trained UNITER model, one enhanced with an image sentiment classifier, whereas the second leverages a Vocabulary Graph Convolutional Network (VGCN). Additionally, we explore an ensemble using the aforementioned models. Our best model reaches an F1-score of 71.4% in Sub-task A and 67.3% for Subtask B positioning our team in the upper third of the leaderboard. We release the code and experiments for our models on GitHub 1 .

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“…They presented a comparative analysis of the performance of different deep learning algorithms over Urdu datasets for sentiment analysis. Ali et al [31] used word filtering and feature selection and optimization techniques to improve the detection of hate speech from Urdu Tweets. They used machine learning algorithms to classify the text.…”

Section: Sentiment Analysis In the Urdu Languagementioning

confidence: 99%

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

Ullah¹,

Talib²,

Rana³

et al. 2022

Computers, Materials &Amp; Continua

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In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users' emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users' emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.

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Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

Cited by 32 publications

References 31 publications

Hate speech detection in social media: Techniques, recent trends, and future challenges

Hate speech detection in social media: Techniques, recent trends, and future challenges

UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

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