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@article{187638,
        author = {Priti Arvind Ram and Jiteshree Raut},
        title = {Multi-language sentiment analysis on social media},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {6984-6986},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187638},
        abstract = {In order to comprehend user thoughts, feelings, and attitudes expressed on social media platforms, sentiment analysis has emerged as a crucial method. However, multilingual content, code-mixed text, and informal writing styles that are frequently seen on social media sites like Facebook, Twitter, Instagram, and YouTube continue to provide challenges for the majority of sentiment analysis algorithms. The goal of this research is to employ multilingual preprocessing, machine learning, and natural language processing (NLP) to create and implement a Multi-Language Sentiment Analysis System for social media. The technology analyzes text in several languages, divides sentiment into neutral, negative, and positive groups, and offers analytical insights. Applications like social media analytics, public opinion mining, and brand monitoring can benefit from the suggested model's increased accuracy for multilingual datasets.},
        keywords = {},
        month = {November},
        }