@inproceedings{ashraf-etal-2022-nayel,
    title = "{NAYEL} @{LT}-{EDI}-{ACL}2022: Homophobia/Transphobia Detection for Equality, Diversity, and Inclusion using {SVM}",
    author = "Ashraf, Nsrin  and
      Taha, Mohamed  and
      Taha, Ahmed  and
      Nayel, Hamada",
    editor = "Chakravarthi, Bharathi Raja  and
      Bharathi, B  and
      McCrae, John P  and
      Zarrouk, Manel  and
      Bali, Kalika  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/sigarab-more-entries-6621/2022.ltedi-1.42/",
    doi = "10.18653/v1/2022.ltedi-1.42",
    pages = "287--290",
    abstract = "Analysing the contents of social media platforms such as YouTube, Facebook and Twitter gained interest due to the vast number of users. One of the important tasks is homophobia/transphobia detection. This paper illustrates the system submitted by our team for the homophobia/transphobia detection in social media comments shared task. A machine learning-based model has been designed and various classification algorithms have been implemented for automatic detection of homophobia in YouTube comments. TF/IDF has been used with a range of bigram model for vectorization of comments. Support Vector Machines has been used to develop the proposed model and our submission reported 0.91, 0.92, 0.88 weighted f1-score for English, Tamil and Tamil-English datasets respectively."
}