Text Classification using Topic Model (TCTM)

Text Clasification using Topic Model (TCTM) is a Java-based package for text classification tool using topic model. This application respond to Windows only until now.

TCTM is made based on MALLET toolkit. This use a SVM classifier from SVM multiclass provided by Thorsten Joachims.

This application's input is a text documents set divided by training/test, and directory should be exist per class. First output is a topic model. Second one is bag of words(BOW) features. Third is joint with topic and BOW. Lastly, we provide a result by accuracy.

Sample input directory scheme : (see the data\fourNewsGroups\ )

• train\class1\documents (Each file are handled one data) \class2\documents \class3\documents - and so on ...
• test \class1\documents \class2\documents \class3\documents - and so on ...

Quick Start Guide

Once you have downloaded and installed TCTM, the easiest way to get started is follow below step. 0. Check directory architecture that we want to classify. -input \data\sampleforTutorial\train* (each directory should be a class(=label) name) \data\sampleforTutorial\test*

1. Topic Modeling Make a model from a whole text corpus. edu.kaist.irlab.topics.tui.Text2VariedTopicModels --input data/sampleforTutorial/total/* --output-dir data/sampleforTutorial/topicmodel

2. Feature Set Generation Make a varied feature set from train and test data and topic models. edu.kaist.irlab.topics.tui.Text2VariedSvmLightFeatures --input-train-dir data/sampleforTutorial/train/* --input-test-dir data/sampleforTutorial/test/* --input-topic-dir data/sampleforTutorial/topicmodel/VTopicModel_Wi100_Di200 --output-dir data/sampleforTutorial/FeatureSet_Wi100_Di200

3. SVM Classification per feature set Excute SVM Multiclass Classification edu.kaist.irlab.classify.tui.ExecuteSvmMulticlass --input-dir data/sampleforTutorial/FeatureSet_Wi100_Di200/*

See more : https://docs.google.com/presentation/d/1emBVYeGFYF4F2Nbp9quiSrksXE-M-iNL8_-bCoiQzgo/edit#slide=id.p