gr.ntua.sentimentanalysis.TrainingThread.java Source code

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Here is the source code for gr.ntua.sentimentanalysis.TrainingThread.java

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/*
*    This file is part of SocIoS Sentiment Analysis Service.
*
*    SocIoS Sentiment Analysis Service is free software: you can redistribute it and/or modify
*    it under the terms of the GNU General Public License as published by
*    the Free Software Foundation, either version 3 of the License, or
*    (at your option) any later version.
*
*    SocIoS Sentiment Analysis Service is distributed in the hope that it will be useful,
*    but WITHOUT ANY WARRANTY; without even the implied warranty of
*    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
*    GNU General Public License for more details.
*
*    You should have received a copy of the GNU General Public License
*    along with SocIoS Sentiment Analysis Service.  If not, see <http://www.gnu.org/licenses/>.
 *
 */

package gr.ntua.sentimentanalysis;

import Classification.VectorModels;
import Temp.SerializationUtilities;
import weka.classifiers.functions.LibLINEAR;

/**
 *
 * @author Fot
 */

public class TrainingThread extends Thread {

    //Thread runner;
    VectorModelSentimentAnalysis a;

    public TrainingThread(VectorModelSentimentAnalysis b) {
        a = b;
    }

    public void run() {
        //Display info about this particular thread
        System.out.println(Thread.currentThread());
        String[] inputPaths = {
                "C:\\FOT\\SentimentAnalysisServiceGraphs\\standfordTrainingSet\\trainingNegativeTweets",
                "C:\\FOT\\SentimentAnalysisServiceGraphs\\standfordTrainingSet\\trainingPositiveTweets" };
        String[][] documents = new String[inputPaths.length][];
        for (int i = 0; i < inputPaths.length; i++) {
            documents[i] = (String[]) SerializationUtilities.loadSerializedObject(inputPaths[i]);
            System.out.println("Documents\t:\t" + documents[i].length);
        }

        System.out.println("Preparing instances...");
        a.vmcl = new VectorModels(0.5, 0.0, documents);
        a.vmcl.prepareData(true, a.REP_MODEL);
        a.instances = a.vmcl.getInstances();

        System.out.println("Training classifier...");
        a.classifier = new LibLINEAR();
        try {

            a.classifier.buildClassifier(a.instances);
        } catch (Exception e) {
            e.printStackTrace();
        }
        System.out.println("Classifier was trained!");
    }
}