Precinct Vaticinator on Social-Media using Machine Learning Techniques

M. Chaitanya Bharathi; Dr. A. Seshagiri Rao; B. Sravani; R. Veeranjaneyulu

doi:10.55524/ijircst.2022.10.4.26

Abstract

Precinct vaticinator of users from online social media brings considerable research these days. Automatic recognition of precinct related with or referenced in records has been investigated for decades. As a standout amongst the online social network organization, Social-Media 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, precinct vaticinator on Social-Media 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 precinct vaticinator using tweets is studied. In particular, tweet precinct 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 precinct of user from the tweet text exploiting machine learning techniques namely naïve bayes, Support Vector Machine and Decision Tree.

Keywords

Social-media, Social-Media, Tweets, precinct vaticinator, Naive-Bayes, Support-Vector-Machine, Decision -Tree, Machine- Learning

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Cites this article as

M. C. Bharathi, D. A. S. Rao, B. Sravani, R. Veeranjaneyulu, "Precinct Vaticinator on Social-Media using Machine Learning Techniques", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-10, no.4, pp.213-217, 2022. Available from: https://doi.org/10.55524/ijircst.2022.10.4.26

Corresponding Author

M. Chaitanya Bharathi

Assistant Professor, Department of Information Technology, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India

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Precinct Vaticinator on Social-Media using Machine Learning Techniques

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