Examples with SentimentClass edu.stanford.nlp.simple.SentimentClass used on opensource projects

Example 1 with SentimentClass

use of edu.stanford.nlp.simple.SentimentClass in project CoreNLP by stanfordnlp.

the class SimpleSentiment method classify.

/**
 * @see SimpleSentiment#classify(CoreMap)
 */
public SentimentClass classify(String text) {
    Annotation ann = new Annotation(text);
    pipeline.get().annotate(ann);
    CoreMap sentence = ann.get(CoreAnnotations.SentencesAnnotation.class).get(0);
    Counter<String> features = featurize(sentence);
    RVFDatum<SentimentClass, String> datum = new RVFDatum<>(features);
    return impl.classOf(datum);
}

Example 2 with SentimentClass

use of edu.stanford.nlp.simple.SentimentClass in project CoreNLP by stanfordnlp.

the class SimpleSentiment method train.

/**
 * Train a sentiment model from a set of data.
 *
 * @param data The data to train the model from.
 * @param modelLocation An optional location to save the model.
 *                      Note that this stream will be closed in this method,
 *                      and should not be written to thereafter.
 *
 * @return A sentiment classifier, ready to use.
 */
@SuppressWarnings({ "OptionalUsedAsFieldOrParameterType", "ConstantConditions" })
public static SimpleSentiment train(Stream<SentimentDatum> data, Optional<OutputStream> modelLocation) {
    // Some useful variables configuring how we train
    boolean useL1 = true;
    double sigma = 1.0;
    int featureCountThreshold = 5;
    // Featurize the data
    forceTrack("Featurizing");
    RVFDataset<SentimentClass, String> dataset = new RVFDataset<>();
    AtomicInteger datasize = new AtomicInteger(0);
    Counter<SentimentClass> distribution = new ClassicCounter<>();
    data.unordered().parallel().map(datum -> {
        if (datasize.incrementAndGet() % 10000 == 0) {
            log("Added " + datasize.get() + " datums");
        }
        return new RVFDatum<>(featurize(datum.asCoreMap()), datum.sentiment);
    }).forEach(x -> {
        synchronized (dataset) {
            distribution.incrementCount(x.label());
            dataset.add(x);
        }
    });
    endTrack("Featurizing");
    // Print label distribution
    startTrack("Distribution");
    for (SentimentClass label : SentimentClass.values()) {
        log(String.format("%7d", (int) distribution.getCount(label)) + "   " + label);
    }
    endTrack("Distribution");
    // Train the classifier
    forceTrack("Training");
    if (featureCountThreshold > 1) {
        dataset.applyFeatureCountThreshold(featureCountThreshold);
    }
    dataset.randomize(42L);
    LinearClassifierFactory<SentimentClass, String> factory = new LinearClassifierFactory<>();
    factory.setVerbose(true);
    try {
        factory.setMinimizerCreator(() -> {
            QNMinimizer minimizer = new QNMinimizer();
            if (useL1) {
                minimizer.useOWLQN(true, 1 / (sigma * sigma));
            } else {
                factory.setSigma(sigma);
            }
            return minimizer;
        });
    } catch (Exception ignored) {
    }
    factory.setSigma(sigma);
    LinearClassifier<SentimentClass, String> classifier = factory.trainClassifier(dataset);
    // Optionally save the model
    modelLocation.ifPresent(stream -> {
        try {
            ObjectOutputStream oos = new ObjectOutputStream(stream);
            oos.writeObject(classifier);
            oos.close();
        } catch (IOException e) {
            log.err("Could not save model to stream!");
        }
    });
    endTrack("Training");
    // Evaluate the model
    forceTrack("Evaluating");
    factory.setVerbose(false);
    double sumAccuracy = 0.0;
    Counter<SentimentClass> sumP = new ClassicCounter<>();
    Counter<SentimentClass> sumR = new ClassicCounter<>();
    int numFolds = 4;
    for (int fold = 0; fold < numFolds; ++fold) {
        Pair<GeneralDataset<SentimentClass, String>, GeneralDataset<SentimentClass, String>> trainTest = dataset.splitOutFold(fold, numFolds);
        // convex objective, so this should be OK
        LinearClassifier<SentimentClass, String> foldClassifier = factory.trainClassifierWithInitialWeights(trainTest.first, classifier);
        sumAccuracy += foldClassifier.evaluateAccuracy(trainTest.second);
        for (SentimentClass label : SentimentClass.values()) {
            Pair<Double, Double> pr = foldClassifier.evaluatePrecisionAndRecall(trainTest.second, label);
            sumP.incrementCount(label, pr.first);
            sumP.incrementCount(label, pr.second);
        }
    }
    DecimalFormat df = new DecimalFormat("0.000%");
    log.info("----------");
    double aveAccuracy = sumAccuracy / ((double) numFolds);
    log.info("" + numFolds + "-fold accuracy: " + df.format(aveAccuracy));
    log.info("");
    for (SentimentClass label : SentimentClass.values()) {
        double p = sumP.getCount(label) / numFolds;
        double r = sumR.getCount(label) / numFolds;
        log.info(label + " (P)  = " + df.format(p));
        log.info(label + " (R)  = " + df.format(r));
        log.info(label + " (F1) = " + df.format(2 * p * r / (p + r)));
        log.info("");
    }
    log.info("----------");
    endTrack("Evaluating");
    // Return
    return new SimpleSentiment(classifier);
}