Browsing by Author "Dr. Waqas Anwar"

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    Multi-Aspect Hate Speech Analysis for Roman Urdu Text
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Aniqa Khalid; SP19-RCS-004; LHR TP 7597; Dr. Waqas Anwar
    We live in the age of technology where a large amount of information is produced daily on social media sites as it becomes a source for expressing their opinions and sharing ideas with other people, it also becomes a place for abusive language, personal attacks, and hateful comments. Determining the nature of the suspension is difficult and time-consuming. Automating the process of hate speech analysis in online conversations is the best way to ensure user security and improve online conversations. In this study, we have produced our dataset for Roman Urdu containing more than 3k comments which were annotated by NLP experts with the following aspects: Hostility, directness, target and group. The dataset is trained using various deep learning & machine learning algorithms for figuring out which model is the best at classifying multi-aspect hate speech. The results showed that logistic regression and bi-LSTM are the best algorithm in determining the toxicity of Roman Urdu text.
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    Multi-Label Toxic Comment Classification of Urdu Language
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Mian Ahmed Shafiq; FA18-RCS-002; LHR TP 6408; Dr. Waqas Anwar
    We are living in a time of technology where a huge amount of information is produced on daily basis on social media websites as it becomes a source to express their views and share ideas with other peoples it also becomes a place for abusive language, personal attacks, and hateful comments. Determining the nature of the comment is difficult and takes a lot of time. Automating the process of detecting toxicity in online comments is the best way to increase user safety and improve online discussions. In this paper, we have produced our dataset of the Urdu language having 20k comments which have been annotated by NLP experts with the following categories: toxic, severe toxic, obscene, threat, insult, and identity hate. The dataset is trained using different machine learning algorithms to find out which model is better in the classification of multi-label toxic comments. Results show that Binary Relevance is the best algorithm in determining the toxicity of comments of the Urdu Language