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@article{181357,
        author = {Binage Varsha Vikas},
        title = {Artificial Intelligence and Language Processing: Implications for Humanities Research},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4561-4566},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181357},
        abstract = {Artificial Intelligence (AI) and Natural Language Processing (NLP) are transforming the field of humanities research by enabling automated text analysis, machine translation, and computational linguistics. These technologies allow scholars to process vast amounts of textual data, uncover linguistic patterns, and enhance accessibility. AI-powered tools such as sentiment analysis, topic modelling, and authorship attribution provide new insights into literature, historical documents, and linguistic studies. Moreover, AI facilitates cross-cultural research by enabling efficient translation and interpretation of texts in multiple languages. However, challenges such as algorithmic bias, ethical concerns, and the limitations of AI in capturing human subjectivity remain critical. The reliance on AI for language processing also raises concerns regarding the potential loss of human interpretative depth and the devaluation of traditional analytical methods. This paper explores both the benefits and challenges of AI in humanities research, emphasizing the need for a balanced approach that integrates AI while preserving the depth of human interpretation. By examining real-world applications, case studies, and ethical considerations, this study aims to highlight the evolving relationship between AI technology and the humanities, offering recommendations for its responsible and effective use.},
        keywords = {Artificial Intelligence (AI), Natural Language Processing (NLP), Humanities Research, Automated Text Analysis, Machine Translation Computational Linguistics, Sentiment Analysis, Topic Modelling, Authorship Attribution, Cross-Cultural Research, Algorithmic Bias, Ethical Concerns, Human Subjectivity},
        month = {June},
        }