# Predicting Location of Tweets Using Machine Learning Approaches
## 6 Conclusions  
In this study, we aimed to investigate location prediction using the content for Arabic tweets from Saudi Arabia using machine learning techniques with and without the utilization of a geo-distance matrix. Through extensive experimentation using a large number of tweets, we were able to achieve high accuracy rates in predicting the location of tweets. The use of a geo-distance matrix is a novel approach for location prediction using tweets. Furthermore, the proposed methodology used to achieve the objectives of this research yielded satisfactory results and can be utilized in real-world applications such as real-time event detection, intelligent transportation systems, location-based recommendations, and monitoring the public health of the citizens. The study also features an extraordinary feature that presents the predicted regions of tweets on the map of Saudi Arabia as shown in Figure 3, and an ability to predict the location of tweets based on textual data alone. However, the focus of the study was limited to Arabic tweets from Saudi Arabia, and, thus, the findings may not be generalizable to tweets in other languages or from other countries. Future research could examine the impact of other forms of data such as images and videos on location prediction, as well as explore the use of other forms of data and examine the results from other countries or languages to acquire a more comprehensive understanding of location prediction using tweets; using a transformer neural network can also be used to produce more accurate results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Location Prediction Using**} & \multicolumn{3}{c}{**Location Prediction Using**} & \multicolumn{3}{c}{**Location Prediction Using**} \\  & \multicolumn{3}{c}{**Tweet Text and**} & \multicolumn{3}{c}{**Tweet Text and**} & \multicolumn{3}{c}{**Tweet Text and**} & \multicolumn{3}{c}{**Home Location,**} \\  & \multicolumn{3}{c}{**Home Location**} & \multicolumn{3}{c}{**Named Entity**} & \multicolumn{3}{c}{**and Named Entity**} \\ \hline
**Distance** & **LR** & **RF** & **NB** & **SVM** & **LR** & **RF** & **NB** & **SVM** & **LR** & **RF** & **NB** & **SVM** \\ \hline
**0** & 50.79 & **64.50** & 37.66 & 54.57 & 23.65 & **41.78** & 21.42 & 27.72 & 53.26 & **64.55** & 42.64 & 55.96 \\ \hline
**20** & 51.64 &  
**Author Contributions:** Conceptualization, M.A., S.A. and M.M.; methodology, M.A., S.A. and M.M.; software, M.M. and K.A.; validation, M.M., K.A. and A.A.; formal analysis, M.A. and S.A; investigation, M.M. and K.A.; resources, M.A. and S.A.; data curation, M.M. and K.A.; writing--original draft preparation, M.A., S.A. and M.M.; writing--review and editing, M.A., S.A., M.M., K.A. and A.A.; visualization, M.M. and K.A; supervision, S.A.; project administration, M.A., S.A., and M.M.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is financially supported by the Deanship of Scientific Research at King Khalid University under research grant number (RGP.2/157/43).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset used in this study was collected by Center for Artificial Intelligence (CAI) at King Khalid University, and it is available from the corresponding authors upon request.  
**Acknowledgments:** This work would not have been possible without the financial support offered by King Khalid University. We would like to express our deepest gratitude for their generous support.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GMM & Gaussian Mixture Mode. \\ VSM & Vector Space Mode. \\ TF-IDF & Term Frequency-Inverse Document Frequency. \\ CNN & Convolutional Neural Network. \\ \end{tabular}  
Figure 3: The predicted locations of the collected tweets.