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@article{152706,
        author = {P. MANISH and N. T. PRIYANKA and CH. USHASWINI and D. SRAVANTHI and G. VENKAT SAI},
        title = {LOCATION PREDICTION ON TWITTER USING MACHINE LEARNING TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {4},
        pages = {160-163},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152706},
        abstract = {Location prediction of users from online social media brings considerable research these days. Automatic recognition of location related with or referenced in records has been investigated for decades. As a standout amongst the online social network organization, Twitter has pulled in an extensive number of users who send a millions of tweets on regular schedule. Because of the worldwide inclusion of its users and continuous tweets, location prediction on Twitter has increased noteworthy consideration in these days. Tweets, the short and noisy and rich natured texts bring many challenges in research area for researchers. In proposed framework, a general picture of location prediction using tweets is studied. In particular, tweet location is predicted from tweet contents. By outlining tweet content and contexts, it is fundamentally featured that how the issues rely upon these text inputs. In this work, we predict the location of user from the tweet text exploiting machine learning techniques namely naïve bayes, Support Vector Machine and Decision Tree.},
        keywords = {online social network organization, Tweets, naïve bayes, Support Vector Machine and Decision Tree.},
        month = {},
        }