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Geo-tags from micro-blog posts have been shown to be useful in many datamining applications. This work seeks to find out if the location type derivedfrom these geo-tags can benefit input methods, which attempts to predict thenext word a user will input during typing. If a correlation between differentlocation types and a change in word distribution can be found, the locationtype information can be used to make the input method more accurate. This workqueried micro-blog posts from Twitter API and location type of these posts fromGoogle Place API, forming a dataset of around 500k samples. A statistical studyon the word distribution found weak support for the assumption. An LSTM basedprediction experiment found a 2% edge in the accuracy from language modelsleveraging location type information when compared to a baseline without thatinformation.

Augmenting Input Method Language Model with user Location Type Information