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DOI

About

In this experiment, the key objective is to infer the user location based on the user contents i.e. infer Twitter user geolocation based on in their tweets. This experiment involves two key tasks.

  1. Feature Engineering
  2. Machine Learning - to predict future unseen data

The tweetlocml application attempt to do mainly on engineering features (or attributes) that can be used to build a classifier model for model-driven approach with Machine Learning algorithms to achieve the project objective. The project mainly focus on Supervised Machine Learning model classifier.

Input Data Assumptions

There are 4 input data that need to be available in the following format.

Gazetteer Dictionary

  1. Download Free Gazetteer data from GeoNames e.g. US.zip
  2. Process it to extract the column asciiname field data only. e.g. US.txt
  3. Optionally, sort the location names and remove duplicates.
  4. Save this gazetteer data in the plain text .txt format and specify it in config.properties, gazetteer=path/to/file.txt

Tweet Data

  1. Harvest Twitter user public tweets data e.g. using twitter4j with Twitter API

  2. And process it in the following format:

     user_id (tab) tweet_id (tab) tweet_text (tab) location_code  (newline)

  3. Save it as plain text .txt format

  4. Use hold-out strategy, split the tweets corpus to 60/20/20 for Training/Development/Test respectively.

  5. And specify them in config.properties, tweets.train, tweets.dev, tweets.test

location_code is 2-letter format of US city names e.g. SD for San Diego. This can be pre-determined from a Twitter user profile or location Long/Lat information through Twitter API. For the test dataset, do label the location_code as question-mark ? instead -- i.e. in order to evaluate as an unseen data.

OpenNLP

  1. Download the OpenNLP model data from http://opennlp.sourceforge.net/README.html#models
  2. Specify opennlp.model.pos.en field in config.properties

Stop words

  1. Download from http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words
  2. Save it as stop_words.txt

Building

tweetlocml uses a couple of 3rd party libraries. Please refer tweetlocml/pom.xml for details.

tweetlocml can build with maven.

cd tweetlocml
mvn clean
mvn test
mvn package

The build artifacts can find under tweetlocml/target folder.

Deployment

The automated deployment is written for Ant build.xml script. You will need to have Apache Ant in your path. Otherwise you can read build script and manually manipulate the deployment structure.

cd tweetlocml
ant clean
ant deploy

Quick Start by using batch scripts

  1. Generate classifier

    • Generate Part-of-speech Noun classifier

        gen-classifier-pos.bat

    • Generate Part-of-speech Noun + English Stemming classifier

        gen-classifier-stem.bat

    • Generate Part-of-speech Noun + Place (Approximate matching with US gazetteer) classifier

        gen-classifier-place.bat

  2. Perform analysis on the generated engineered features. (Somewhat empirical analysis)

  3. Select the attributes and save into attributes.txt file (configurable in config.properties).

  4. Generate Weka arff file with frequency count numeric feature vector for tweets:

    • Training data:

        gen-arff-train.bat

    • Dev data:

        gen-arff-dev.bat

    • Test data:

        gen-arff-test.bat

  5. Perform Machine Learning in Weka.

  6. Repeat until (at your) best representation classifier found for the problem.

Configuration

  1. Open config.properties and configure all the paths.
  2. Adjust other parameters. Default values are a good starting point.

Running First Time

If config is not under the same root as where tweetlocml.jar is, then pass -c option.

java -jar tweetlocml.jar -c /path/to/config.properties  [... and other options]

Preprocess Gazetteer (Run Once)

java -jar tweetlocml.jar -d 1 --preprocess gaze

Partition Tweets (Optional, if Tweets corpus is big)

java -jar tweetlocml.jar --preprocess parted

Dry Run

To dry run first few tweets with pos - Part-of-speech classifier

java -jar tweetlocml.jar -d 3

Classifiers

java -jar tweetlocml.jar -a pos -d 2
  • pos = Part-of-speech classifier (default if no -a is pass)
  • stem = Part-of-speech +Stemmming classifier
  • place = Part-of-speech +Place classifier - might take time to run

Output file

Use -o to specify custom output file. Otherwise default to output_[CITY].csv format.

java -jar tweetlocml.jar -a place -o output_place.csv -d 5
java -jar tweetlocml.jar -a place -o directory/output_place.csv -d 5

Start index at 123 and run 10 more lines

java -jar tweetlocml.jar -a stem -o output_stem.csv -i 123 -d 10

Generate Weka arff from the selected attributes after analysis

Configure attributes file location in config.properties. The default is: attributes=attributes.txt

Generate for training set:

java -jar tweetlocml.jar --arff train

Generate for development set:

java -jar tweetlocml.jar --arff dev

Generate for test set:

java -jar tweetlocml.jar --arff test

It will generate frequency numeric attributes and the respective vectors for each tweet raw data.

Binding the parted classifier output files

Running the place classifier will take long time due to each term approximate matching to location dictionary. The approach is to make partition for tweets dataset. The default is 5% each. This can change at config.properties. Each parted files can run concurrently on multiple servers. At the end, these parted files need to bind back to create the final master file. In this case, unique user count information is lost due to partition. To bind the parted files, run with --mastering option with point to the first partition directory. Program will auto discover the rest of partitions and bind to final master file for each city.

java -jar tweetlocml.jar --mastering ./place/parted/train00

Running As a Job

It is good idea to run with screen on Linux.

screen
java -jar tweetlocml.jar -a pos &
[ctrl + a, d]
tail -f app.log
[ctrl + c]

Notes

This assignment work is done for COMP90049 Project 2 assessment 2016 SM2, The University of Melbourne. You can read the report on background context, though it discusses more on the data that I have worked with. You may also want to read the related tweetloc assignment. The implementation still has room for improvement. You may cite this work as follows.

Zenodo:

DOI

LaTeX/BibTeX:

@misc{sanl1,
    author    = {Lin, San Kho},
    title     = {tweetlocml - Geolocation of Tweets with Machine Learning},
    year      = {2016},
    url       = {https://github.com/victorskl/tweetlocml},
    urldate   = {yyyy-mm-dd}
}

Further Improvement Pointers:

  1. tweetlocml can better streamline with Weka library i.e. bypass Weka GUI and call/use it through programming API.
  2. tweetlocml can also streamline with twitter4j for acquiring tweets.

This could achieve high performance distributed application that run on BigData middleware and frameworks.