# geolocation

**Repository Path**: mmmz2/geolocation

## Basic Information

- **Project Name**: geolocation
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-26
- **Last Updated**: 2024-07-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

#Geolocation of Social Media Users Using Text and Network Information
Afshin Rahimi, arahimi@student.unimelb.edu.au


This tool implements the geolocation models described in the following two papers:

```
@InProceedings{rahimi2015exploiting,
author="Rahimi, Afshin and Vu, Duy and Cohn, Trevor and Baldwin, Timothy",
title="Exploiting Text and Network Context for Geolocation of Social Media Users",
booktitle="Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
year="2015",
publisher="Association for Computational Linguistics",
pages="1362--1367",
location="Denver, Colorado",
url="http://aclweb.org/anthology/N15-1153"
}
```

```
@InProceedings{rahimi2015twitter,
author="Rahimi, Afshin and Cohn, Trevor and Baldwin, Timothy",
title="Twitter User Geolocation Using a Unified Text and Network Prediction Model",
booktitle="Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
year="2015",
publisher="Association for Computational Linguistics",
pages="630--636",
location="Beijing, China",
url="http://aclweb.org/anthology/P15-2104"
}
```

##Models
The models include text-based classification, network-based label propagation (regression)
and network-based label propagation (classification).
To run a model add the model name from params.all_models to the params.models_to_run list.


##Data
###Clusters (regions) for classification models
For the classification models, the real-valued coordinates of the training points are clustered using
[k-d tree](https://github.com/utcompling/textgrounder/blob/master/src/main/java/ags/utils/KdTree.java) and each cluster (region) is assigned a label. This implementation expects the clusters to be
written into a text file where training point members of each cluster are written in one line tab-separated.
For example int the following example we have 2 clusters (regions) each with two training points:


    lat1,lon1	lat2,lon2
    lat3,lon3	lat4,lon4

###Training, development and test input files
This program expects three separate training, dev and test gzipped files in each, a user is represented as a line with the following
format:

    username	latitude	longitude	aggregated-tweets-of-user-including-mentions

    
Note: The datasets used are different from those used in:
Wing, Benjamin, and Jason Baldridge. "Hierarchical Discriminative Classification for Text-Based Geolocation." EMNLP. 2014.
in that we have rebuilt the datasets to include mention information.

##Configuration
All the configuration parameters are in params.py which should be changed according to the environment.