LOCATION PREDICTION ON TWITTER USING MACHINE LEARNING TECHNIQUES
Authors:
Ch.Lalitha, Dr.V.Bhaskar Murthy
Page No: 576-582
Abstract:
The prediction of user locations based on online social media has become an area of significant research. The automatic identification of locations mentioned or referenced in posts has been explored for decades. Among the various social network platforms, Twitter stands out due to its large user base, with millions of tweets being posted daily. Given the global reach of its users and the real-time nature of its tweets, location prediction on Twitter has gained considerable attention in recent years. Tweets, which are brief, noisy, and rich in content, present unique challenges for researchers in this domain. This paper explores a framework for location prediction using tweets, focusing on predicting user location based on tweet content. By analyzing the text and context of tweets, we highlight the various challenges associated with these inputs. In this study, we apply machine learning techniques, including Naive Bayes, Support Vector Machine (SVM), and Decision Tree, to predict user locations from tweet texts.
Description:
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Volume & Issue
Volume-14,Issue-4
Keywords
Keywords—Social Media, Twitter, Tweets, Location Prediction, Naive Bayes, Support Vector Machine, Decision Tree, Machine Learning.