public void run(ArrayList<Tweet> tweet, DocumentProperties dp) throws Exception {
System.out.println("running analysis...");
FileWriter fr =
new FileWriter(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat(".libsvm")));
for (int i = 0; i < tweet.size(); i++) {
int ind = 1;
if (tweet.get(i).label.equals("positive")) fr.append("1 ");
else if (tweet.get(i).label.equals("negative")) fr.append("-1 ");
else if (tweet.get(i).label.equals("neutral")) fr.append("0 ");
for (int num : tweet.get(i).unifeatures) {
fr.append(ind + ":" + num + " ");
ind++;
}
fr.append(String.valueOf(ind++ + ":" + tweet.get(i).prob[0]) + " ");
fr.append(String.valueOf(ind++ + ":" + tweet.get(i).prob[1]) + " ");
fr.append(String.valueOf(ind++ + ":" + tweet.get(i).prob[2]) + " ");
fr.append(String.valueOf(ind++ + ":" + tweet.get(i).aifnnScore) + " ");
fr.append(String.valueOf(ind++ + ":" + tweet.get(i).sentiScore) + " ");
fr.append("\n");
}
fr.close();
}
public void runFilter() throws Exception {
System.out.println("filtering attributes...");
System.out.println("running weka filters and weka-libsvm");
File svmfile = new File(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat(".libsvm")));
LibSVMLoader libl = new LibSVMLoader();
libl.setFile(svmfile);
Instances data = libl.getDataSet();
NumericToNominal nm = new NumericToNominal(); // Converting last index
// attribute to type
// nominal from numeric
nm.setAttributeIndices("last"); // as the last index would be class
// label for the data
nm.setInputFormat(data);
filteredData = Filter.useFilter(data, nm); // filtered data stored in
// new Instances object
AttrNo = filteredData.numAttributes(); // number of attributes in given
// file
RecordNo = filteredData.numInstances(); // Number of records in given
// file
lowerBound = 0;
upperBound = AttrNo - 1;
AttributeSelection atsl = new AttributeSelection();
Ranker search = new Ranker();
InfoGainAttributeEval infog = new InfoGainAttributeEval(); // Applying
// Attribute
// Selection
// using
// InfoGain
// evaluator
// with
// Ranker
// search
atsl.setEvaluator(infog);
atsl.setSearch(search);
atsl.SelectAttributes(filteredData);
InfoGain = atsl.rankedAttributes();
SelectedAttributes = atsl.selectedAttributes();
// count non zero infoGain
int count = 0;
for (int i = 0; i < InfoGain.length; i++) {
count = (InfoGain[i][1] > 0) ? count + 1 : count;
}
System.out.println("writing attributes with non-zero InfoGain...");
FileWriter svmout =
new FileWriter(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_new.libsvm")));
for (int i = 0; i < RecordNo; i++) {
int index = 1;
svmout.write((int) filteredData.instance(i).value(filteredData.classIndex()) + " ");
for (int j = 0; j < count; j++) {
svmout.write(
index + ":" + (int) filteredData.instance(i).value((int) InfoGain[j][0]) + " ");
index++;
}
svmout.write("\n");
}
svmout.close();
// filtered
File newsvm = new File(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_new.libsvm")));
LibSVMLoader liblnew = new LibSVMLoader();
liblnew.setFile(newsvm);
Instances newdata = liblnew.getDataSet();
nm = new NumericToNominal(); // Converting last index attribute to type
// nominal from numeric
nm.setAttributeIndices("last"); // as the last index would be class
// label for the data
nm.setInputFormat(newdata);
Instances filteredDataNew = Filter.useFilter(newdata, nm); // filtered
// data
// stored in
// new
// Instances
// object
// test file
File newsvmtest =
new File(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_test.libsvm")));
LibSVMLoader libltest = new LibSVMLoader();
libltest.setFile(newsvmtest);
Instances newdatatest = libltest.getDataSet();
nm = new NumericToNominal(); // Converting last index attribute to type
// nominal from numeric
nm.setAttributeIndices("last"); // as the last index would be class
// label for the data
nm.setInputFormat(newdatatest);
Instances filteredDataTest = Filter.useFilter(newdatatest, nm); // filtered
// data
// stored
// in
// new
// Instances
// object
// weka.classifiers.functions.LibSVM -S 0 -K 2 -D 3 -G 0.0 -R 0.0 -N 0.5
// -M 40.0 -C 1.0 -E 0.001 -P 0.1 -seed 1
String[] options = new String[1];
options[0] = "-S 0 -K 2 -D 3 -G 0.1 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -seed 1 -h 0";
System.out.println("building classifier...");
LibSVM svm_model = new LibSVM();
svm_model.setOptions(options); // set the options
svm_model.buildClassifier(filteredData); // build classifier
DecimalFormat df = new DecimalFormat("0.00");
System.out.println("running cross validation...");
Evaluation eval = new Evaluation(filteredData);
// eval.crossValidateModel(svm_model, filteredDataNew, 10, new
// Random(1));
eval.evaluateModel(svm_model, filteredDataTest);
FileWriter results =
new FileWriter(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_results.txt")));
results.write("Classifier 1: Support Vector Machines\n");
results.write("Positive class precision: " + df.format(eval.precision(0)) + "\n");
results.write("Positive class recall: " + df.format(eval.recall(0)) + "\n");
results.write("Positive class f-score: " + df.format(eval.fMeasure(0)) + "\n");
results.write("Negative class precision: " + df.format(eval.precision(0)) + "\n");
results.write("Negative class recall: " + df.format(eval.precision(0)) + "\n");
results.write("Negative class f-score: " + df.format(eval.fMeasure(0)) + "\n");
System.out.println("generating results...");
System.out.println("*" + sentiAnalysis.outout + "*\t" + "\tPositive\tNegative\tNeutral");
System.out.println(
"Precision\t"
+ df.format(eval.precision(0))
+ "\t"
+ df.format(eval.precision(2))
+ "\t"
+ df.format(eval.precision(1)));
System.out.println(
"Recall\t"
+ df.format(eval.recall(0))
+ "\t"
+ df.format(eval.recall(2))
+ "\t"
+ df.format(eval.recall(1)));
System.out.println(
"F-score\t"
+ df.format(eval.fMeasure(0))
+ "\t"
+ df.format(eval.fMeasure(2))
+ "\t"
+ df.format(eval.fMeasure(1)));
results.close();
}
