Location Prediction Using Data Mining Techniques

Abstract

The rapid use of social media made location prediction the key to research studies based on-location services such as, advertising, recommendations, climatological forecast, and security system. Locations are the center of information for these applications. According to millions of users who post tweets every day, the geographical coordinates are often hidden in Twitter due to privacy reasons. Identifying the home location of Twitter users is very important in many business community applications. Therefore, many approaches have been developed to automatically geolocate Twitter users using their tweets. Depending on the importance of catching the location of the users and the rapid usage of Twitter, Location prediction on Twitter has been a point of research in many studies. This thesis work provides a comprehensive overview of the prediction of the user's location on Twitter, which focuses on the home location prediction and tweet location prediction. This is achieved by defining the inputs of these two research views that are content, network, and context, and then proposing two new location prediction models. The First proposed model is to predict the tweet location based on the KNN-Sentimental Analysis (KNNSA) model. Predicting the tweet location based on the KNN-sentiment analysis (KNNSA) extracts text features from the tweet in addition to the date and time features. Then, applying sentimental analysis and classifying the data by K-nearest neighbors (KNN) classifier. The (KNNSA) is evaluated and compared to the previous work and it achieves better performance in terms of root mean squared error (RMSE) and of the mean absolute error (MAE).