Twitter Geolocation Predictor

This is a deep-learning tool to predict the location of a Twitter user based solely on the text content of his/her tweets without any other form of metadata.

Overview

The Twitter Geolocation Predictor is a Recurrent Neural Network classifier. Every training sample is a collection of tweets labeled with a location (e.g. country, state, city, etc.). The model will tokenize all tweets into a sequence of words, and feed them into an Embedding Layer. The embeddings will learn the meaning of words and use them as input for two stacked Long-Short Term Memory layers. A Softmax fully-connected layer at the end yields the classification result.

Getting Started

Dependencies

1. Python 3.5
2. tensorflow
3. keras
4. nltk
5. pandas
6. numpy
7. sqlalchemy
8. sklearn
9. psycopg2

Installation

Clone the repository and install all the dependencies using pip.

$ git clone [email protected]:jmatias/uiuc-twitter-geolocation.git
$ cd uiuc-twitter-geolocation
$ sudo pip3 install -r requirements.txt

This will install the latest CPU version of Tensorflow. If you would like to run on a GPU, follow the Tensorflow-GPU installation instructions.

Using A Pre-Processed Dataset

The tool comes with a built-in dataset of ~430K users located in the U.S. (~410K for training, ~10K for development and ~10K for testing). To train a model using this dataset, run the train.py sample script.

Note: The dataset has a size of approximately 2.5GB.

$ python3 train.py --epochs 5 --batch_size 32 --vocab_size 20000 --hidden_size 100 --max_words 100 --classifier state

Using TensorFlow backend.
Downloading data from https://dl.dropbox.com/s/ze4ov5j30u9rf5m/twus_test.pickle
55181312/55180071 [==============================] - 11s 0us/step
Downloading data from https://dl.dropbox.com/s/kg09i1z32n12o98/twus_dev.pickle
57229312/57227360 [==============================] - 12s 0us/step
Downloading data from https://dl.dropbox.com/s/0d4l6jmgguzonou/twus_train.pickle
2427592704/2427591168 [==============================] - 486s 0us/step

Building model...
Hidden layer size: 100
Analyzing up to 100 words for each sample.
Building tweet Tokenizer using a 20,000 word vocabulary. This may take a while...
Tokenizing tweets from 59,546 users. This may take a while...
Training model...
Train on 50000 samples, validate on 9546 samples
Epoch 1/1
    1664/50000 [..............................] - ETA: 3:59 - loss: 3.8578 - acc: 0.0950 - top_5_acc: 0.2536

You can also try using this data from your own source code.

In [1]: from twgeo.data import twus_dataset
Using TensorFlow backend.

In [2]: x_train, y_train, x_dev, y_dev, x_test, y_test = twus_dataset.load_state_data()

In [3]: x_train.shape
Out[3]: (410336,)

In [4]: y_train.shape
Out[4]: (410336,)

In [5]: x_train, y_train, x_dev, y_dev, x_test, y_test = twus_dataset.load_state_data(size='small')

In [6]: x_train.shape
Out[6]: (50000,)

In [7]: y_train.shape
Out[7]: (50000,)

Pre-Processing your own data

Tweet Text	Location
Hello world! This is a tweet. <eot> This is another tweet. <eot>	Florida
Going to see Star Wars tonite!	Puerto Rico
Pizza was delicious! <eot> I'm another tweeeeeet <eot>	California

Given a raw dataset stored in a CSV file like the one shown above, we can preprocess said data using twgeo.data.input.read_csv_data(). This function will:

1. Tokenize the tweet text.
2. Limit repeated characters to a maximum of 2. For example: 'Greeeeeetings' becomes 'Greetings'.
3. Perform Porter stemming on each token.
4. Convert each token to lower case.

The location data may be any string or integer value.

import twgeo.data.input as input
tweets, locations = input.read_csv_data('mydata.csv', tweet_txt_column_idx=0, location_column_idx=1)

Training the Model

from twgeo.models.geomodel import Model
from twgeo.data import twus

# x_train is an array of text. Each element contains all the tweets for a given user.
# y_train is an array of integer values, corresponding to each particular location we want to train against.
x_train, y_train, x_dev, y_dev, x_test, y_test = twus.load_state_data(size='small')

# num_outputs is the total number of possible classes (locations). In this example, 50 US states plus 3 territories.
# time_steps is the total number of individual words to consider for each user.
# Some users have more tweets then others. In this example, we are capping it at a total of 500 words per user.
geoModel = Model(batch_size=64)
geoModel.build_model(num_outputs=53, time_steps=500,vocab_size=20000)

geoModel.train(x_train, y_train, x_dev, y_dev, epochs=5)
geoModel.save_model('mymodel')

Making Predictions

In [1]: from twgeo.models.geomodel import Model
Using TensorFlow backend.

In [2]: from twgeo.data import twus_dataset as twus

In [3]: x_train, y_train, x_dev, y_dev, x_test, y_test = twus.load_state_data(size='small')

In [4]: geoModel = Model()

In [5]: geoModel.load_saved_model('mymodel')
Loading saved model...

In [6]: geoModel.predict(x_test)
Out[6]: array(['CA', 'FL', 'NY', ..., 'TX', 'MA', 'KY'], dtype=object)

Results

The built-in TWUS dataset was used to train US State and US Census Region classifiers. Using a hidden layer size of 300 neurons, timestep window of 500 words and a vocabulary size of 50,000 words, the model achieves the following results.

Classification Task	Test Set Accuracy	Test Set Accuracy @ 5
US Census Region	73.95%	N/A
US State	51.44%	75.39%