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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences
Basha, Peerbasha Shebbeer
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Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Empowering low-resource languages: a machine learning approach to Tamil sentiment classification Abdul Samad, Saleem Raja; Ganesan, Pradeepa; Rajasekaran, Justin; Radhakrishnan, Madhubala; Basha, Peerbasha Shebbeer; Kuppusamy, Varalakshmi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp941-949

Abstract

Sentiment analysis is essential for deciphering public opinion, guiding decisions, and refining marketing strategies. It plays a crucial role in monitoring public sentiment, fostering customer engagement, and enhancing relationships with businesses' target audiences by analyzing emotional tones and attitudes in vast textual data. Sentiment analysis is extremely limited, particularly for languages like Tamil, due to limited application in diverse linguistic contexts with fewer resources. Given its global impact and linguistic diversity, addressing this gap is crucial for a more nuanced understanding of sentiments in India. In the context of Tamil, the need for sentiment analysis models is particularly crucial due to its status as one of the classical languages spoken by millions. The cultural, social, and historical nuances embedded in Tamil language usage require tailored sentiment analysis approaches that can capture the subtleties of sentiment expression. This paper introduces a novel method that assesses the performance of various text embedding methods in conjunction with a range of machine learning (ML) algorithms to enhance sentiment classification for Tamil text, with a specific focus on lyrics. Experiments notably emphasize FastText word embedding as the most effective method, showcasing superior results with a remarkable 78% accuracy when coupled with the support vector classification (SVC) model.
A hybrid features based malevolent domain detection in cyberspace using machine learning Samad, Saleem Raja Abdul; Ganesan, Pradeepa; Al-Kaabi, Amna Salim Rashid; Rajasekaran, Justin; Singaravelan, Murugan; Basha, Peerbasha Shebbeer
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp916-927

Abstract

The rise of social media has changed modern communication, placing information at our fingertips. While these developments have made our lives easier, they have also increased cybercrime. Cyberspace has become a refuge for modern cybercriminals to conduct destructive actions. Most cyberattacks are carried out through malicious links shared on social media platforms, emails, or messaging services. These attacks can have serious consequences for individuals and organizations, including financial losses, sensitive data breaches, and damage to reputation. Early identification and blocking of such links are crucial to protecting internet users and securing cyberspace. Current research uses machine learning (ML) algorithms to detect malicious hyperlinks based on observed patterns in uniform resource locators (URLs) or web content. However, cyberattack tactics are constantly changing. To address this challenge, this paper introduces a robust method that performs a fine-grained analysis of URLs for classification. Lexical and n-gram features are examined separately, with URL n-grams represented using Word2Vec embeddings. The results from hybrid feature sets are combined using a logistic regression (LR) model to increase overall classification accuracy. This robust method allows the system to use both the structural components of the URL and the fine-grained patterns obtained by the n-grams.
Co-Authors Abdul Samad, Saleem Raja Al-Kaabi, Amna Salim Rashid Ganesan, Pradeepa Kuppusamy, Varalakshmi Radhakrishnan, Madhubala Rajasekaran, Justin Samad, Saleem Raja Abdul Singaravelan, Murugan