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@article{166490,
        author = {Mrs.R.Valliyammal and Mrs.S.Nusrath Najeeba and Ms.A.Nandhini},
        title = {Python-based Text Language Identification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {1147-1150},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166490},
        abstract = {Language detection plays a crucial role in natural language processing (NLP) applications, enabling tasks such as content filtering, language-specific text analysis, and multilingual content management. This paper presents an exploration of text language detection techniques using Python, focusing on practical implementations and comparative evaluations of popular libraries and methods. We begin with an overview of the importance of language detection in diverse NLP contexts. Subsequently, we delve into the technical aspects, discussing methodologies such as character n-grams, probabilistic language models, and machine learning classifiers. A detailed comparative analysis of prominent Python libraries, including NLTK, TextBlob, and LangDetect, highlights their strengths, weaknesses, and suitability for different use cases. Finally, we offer recommendations for selecting appropriate tools based on specific application needs. This paper serves as a comprehensive guide for researchers and practitioners seeking effective language detection solutions using Python in real-world applications.},
        keywords = {Character N-grams, Content Filtering, LangDetect, TextBlob.},
        month = {July},
        }