Faculty Publications
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Item Overlapping word removal is all you need: revisiting data imbalance in hope speech detection(Taylor and Francis Ltd., 2024) RamakrishnaIyer LekshmiAmmal, H.; Ravikiran, M.; Nisha, G.; Balamuralidhar, N.; Madhusoodanan, A.; Anand Kumar, A.K.; Chakravarthi, B.R.Hope speech detection is a new task for finding and highlighting positive comments or supporting content from user-generated social media comments. For this task, we have used a Shared Task multilingual dataset on Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) for three languages English, code-switched Tamil and Malayalam. In this paper, we present deep learning techniques using context-aware string embeddings for word representations and Recurrent Neural Network (RNN) and pooled document embeddings for text representation. We have evaluated and compared the three models for each language with different approaches. Our proposed methodology works fine and achieved higher performance than baselines. The highest weighted average F-scores of 0.93, 0.58, and 0.84 are obtained on the task organisers{'} final evaluation test set. The proposed models are outperforming the baselines by 3{\%}, 2{\%} and 11{\%} in absolute terms for English, Tamil and Malayalam respectively. © 2023 Informa UK Limited, trading as Taylor & Francis Group.Item The Effect of Phrase Vector Embedding in Explainable Hierarchical Attention-Based Tamil Code-Mixed Hate Speech and Intent Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Sharmila Devi, V.S.; Subramanian, S.; Anand Kumar, A.K.The substantial growth in social media users has led to a significant increase in code-mixed content on social media platforms. Millions of users on these platforms upload pictures and videos and post comments regarding their recent or exciting activities. Responding to this uploaded content, a few users occasionally use offensive language to insult others or specific groups. Social media platforms encounter challenges identifying and removing hate speech and objectionable content in various languages. Hate speech, in its general sense, refers to harmful posts directed at individuals or groups based on factors such as their sexuality, religion, community affiliation, disability, and others. Typically, offensive language is directly or indirectly utilized in hate speech posts to insult someone, causing psychological distress to users. In light of this, we propose developing a system to automatically block, remove, or report posts written in code-mixed Tamil containing hate speech. We have gathered code-mixed Tamil comments from Twitter and the Helo App, categorizing them as hate speech and classifying their intent. We have identified three categories of hate speech intent, namely Targeted Individual (TI), Targeted Group (TG), and Others (O). The Targeted Individual (TI) class encompasses posts aimed at a specific individual target. At the same time, the Targeted Group (TG) category primarily focuses on identifying people based on their religion, community, gender, and other characteristics. The Others (O) category encompasses untargeted offensive posts and other posts containing offensive language. In this context, we propose using a phrase-based, Explainable Hierarchical Attention model for hate speech detection. The results demonstrate that the proposed method is more effective in identifying and explaining hate speech and offensive language in social media posts. © 2013 IEEE.