Department of Computer Science & Engineering

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Browsing Department of Computer Science & Engineering by Author "Rajak, Santosh"

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    An Empirical Study on Multi-class Classification of Hostile Posts in Hindi Language
    (Department of Computer Science & Engineering, NITS, 2025-07-30) Rajak, Santosh
    The exponential growth of social media usage has intensified the dissemination of hostile content online, posing significant risks to individuals and communities. While consider able research has focused on hostility detection in English, other linguistically rich but under-resourced languages such as Hindi remain severely neglected due to the lack of high-quality annotated datasets and specialized classification models. This thesis ad dresses these challenges by presenting a holistic framework for the detection of hostile content in Hindi, with contributions spanning dataset creation, model development, and architectural innovation. The first major contribution is the development of a comprehensive and fine-grained hostility detection dataset comprising approximately 8300 Hindi tweets manually anno tated into five distinct classes: Hate, Abusive, Defamation, Offensive, and Non-Hostile. To exploit this dataset effectively, we developed a suite of baseline and hybrid models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), Multi-Layer Perceptrons (MLP), and Decision Trees. These were trained using multilingual embeddings such as GloVe, MuRIL, and XLM-RoBERTa, and fur ther enhanced with emoji-based semantic features to improve contextual interpretation. The hybrid models were fine-tuned to address challenges inherent in Hindi text, such as morphological complexity, and ambiguous phrasing. We leveraged MuRIL’s pooled output to improve Hindi text representation, benefiting from its multilingual understanding and proposed novel hybrid models by combining RNN and CNN architectures with traditional machine learning classifiers. As a methodological advancement, this thesis proposes the Scaled Multi-Head Atten tion–CNN Fusion Model (SMA-CFM), a novel architecture that combines Transformer based attention with convolutional layers to capture both long-range dependencies and local linguistic features from textual as well as emoji features. A trainable attention scaling mechanism is introduced within the multi-head attention block to dynami cally prioritize hostility-relevant tokens. To further enhance classification robustness, a multi-model ensemble strategy integrates feature representations from neural back bones using Random Forest and weighted voting mechanisms. This research makes significant contributions to multilingual NLP by offering a scal able, high-performance framework for hostile content detection in Hindi. It highlights the importance of combining dataset granularity, linguistic contextualization, and ar chitectural innovation to address challenges in low-resource language environments. The dataset, models, and methodologies presented herein lay a robust foundation for future advancements in socially responsible AI and content moderation technologies.