Significance of Linguistic Indicators for Location Prediction

2020. Article 19434882.

Rehman F., Malik M. S., Faisal M. S.

Twitter is one of the most widely and frequently
use microblog. Thousands of tweets are being posted daily on
twitter. With the tweet, peoples are share their current location
using check-ins point of interest that is known as geo-tagged
tweet. However, most of the people not share their locations with
the tweets. Location prediction is beneficial for various fields
such as targeted advertising, rescue operations during a disaster
and recommendation, etc. On the other hand, location
prediction from the text of the tweet is a difficult task as it
encounters many problems such as spelling mistakes, nonstandard
English, etc. In this study, we propose a new set of
linguistic features to predict locations from the texts of the tweet
more effectively. Two twitter datasets, five popular ML models
and five evaluation metrics are used in experiments to explore
the impact of the proposed features. The experimental results
reveal that proposed set of features outperform as compared to
baseline features. In addition, AUC also demonstrates the
significance of the proposed features in the form of 59.94 % and
69.65% using MSM2013 and Ritter dataset respectively. The
findings indicate that proposed set of features are the most
significant indicators and has proved their effectiveness and
strong relationship with location prediction.

Keywords: location, linguistic, Analytic thinking, Space, relative words