This repository contains the source code for RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation.
To run our code, please use the following commands:
g++ RATE.cpp -o RATE -std=c++11
./RATE [Training File] [Test File] [L, optional, default = 30] [T, optional, default = 1]
For example,
g++ RATE.cpp -o RATE -std=c++11
./RATE Dataset/train.txt Dataset/test.txt 40 1
The prediction results will be in ./result.txt (the first row is the classification result). Then you can run
python eval.py
to obtain evaluation metrics.
We release the Europe dataset (Dataset/data.json), where each line is a json file with tweet text and metadata. Due to privacy issues, we have anonymized the whole dataset by representing each word/feature as an integer. An example is shown below.
{
"label":0,
"language":"3",
"timezone":"5",
"offset":"7",
"userlang":"5",
"latitude":"36.8901",
"longitude":"30.6809",
"text":"3332 2608 29"
}
Given the json file, one can run
cd Dataset/
python preprocess.py
to get training and testing data (Dataset/train.txt and Dataset/test.txt).
| Method | Micro-F1 (Acc) | Macro-F1 | Mean Distance Error (km) | Acc@161 |
|---|---|---|---|---|
| RATE | 0.8905 | 0.5230 | 365.16 | 0.4315 |
If you find this repository useful, please cite the following paper:
@inproceedings{zhang2017rate,
title={RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation},
author={Zhang, Yu and Wei, Wei and Huang, Binxuan and Carley, Kathleen M and Zhang, Yan},
booktitle={CIKM'17},
pages={2423--2426},
year={2017}
}