public ClassificationResult Test()
{
var result = new ClassificationResult("Test");
foreach (var entry in dataSet.TestData)
{
var newX = SVMProblemBuilder.CreateNode(entry.stemmedClaim, dataSet.Vocabulary);
var predictedSentiment = model.Predict(newX);
var probability = model.PredictProbabilities(newX);
result.AddPrediction(entry.topicId, probability.Max(pair => pair.Value), entry.IsPredictionCorrect(predictedSentiment));
}
return(result);
}
private static void Test(DataSet dataSet, IStemmer stemmer, string word2VecPath, bool cacheResults)
{
var progress = new ProgressReporting(dataSet.TestData.Count);
var sentimentClassifier = new ClaimSentimentIdentificator(stemmer);
var targetIdentificator = new TargetIdentificator(word2VecPath);
var contrastClassifier = new ContrastClassifier();
var targetResult = new ClassificationResult("Target Identification");
var simpleSentimentResult = new ClassificationResult("Simple Sentiment");
var simpleSentimentWithoutContrastResult = new ClassificationResult("Simple Sentiment without Contrast");
var sentimentResult = new ClassificationResult("Sentiment");
var sentimentWithoutContrastResult = new ClassificationResult("Sentiment without Contrast");
var sentimentAndConstrastResult = new ClassificationResult("Sentiment and Contrast");
var sentimentWithKnownTargetResult = new ClassificationResult("Sentiment with known target");
var sentimentAndConstrastWithKnownTargetResult = new ClassificationResult("Sentiment and Contrast with known Target");
foreach (var entry in dataSet.TestData)
{
//target identificator
var target = targetIdentificator.GetNounPhrase(entry.claim, entry.target);
targetResult.AddPrediction(entry.topicId, 1, IsSignificantlyOverlapping(target.PhraseArray, entry.claimTarget));
//simple sentiment analyzer
double simpleSentiment = sentimentClassifier.GetUnweightedSentiment(entry);
double confidence = Math.Abs(simpleSentiment);
bool isCorrect = entry.IsPredictionCorrect(simpleSentiment);
simpleSentimentResult.AddPrediction(entry.topicId, confidence, isCorrect);
//simple sentiment (ignoring contrast)
isCorrect = entry.IsPredictionCorrect(simpleSentiment * entry.targetsRelation);
simpleSentimentWithoutContrastResult.AddPrediction(entry.topicId, confidence, isCorrect);
//targeted sentiment analyzer
double sentiment = sentimentClassifier.GetTargetedSentiment(entry, target.PhraseArray);
confidence = Math.Abs(sentiment);
isCorrect = entry.IsPredictionCorrect(sentiment);
sentimentResult.AddPrediction(entry.topicId, confidence, isCorrect);
//targeted sentiment analzyer + known contrast
confidence *= contrastClassifier.GetConsistency(entry);
sentimentAndConstrastResult.AddPrediction(entry.topicId, confidence, isCorrect);
//targeted sentiment (ignoring contrast)
isCorrect = entry.IsPredictionCorrect(sentiment * entry.targetsRelation);
sentimentWithoutContrastResult.AddPrediction(entry.topicId, confidence, isCorrect);
//targeted sentiment analyzer but using known target
sentiment = sentimentClassifier.GetTargetedSentiment(entry, entry.claimTarget);
confidence = Math.Abs(sentiment);
isCorrect = entry.IsPredictionCorrect(sentiment);
sentimentWithKnownTargetResult.AddPrediction(entry.topicId, confidence, isCorrect);
//targeted sentiment analyzer but using known target + known contrast
confidence *= contrastClassifier.GetConsistency(entry);
sentimentAndConstrastWithKnownTargetResult.AddPrediction(entry.topicId, confidence, isCorrect);
progress.Increment();
}
if (cacheResults)
{
targetIdentificator.SaveCache();
}
//unigrams svm
OutputBaselineResults(dataSet, 3);
Console.WriteLine(targetResult);
Console.WriteLine(simpleSentimentResult);
Console.WriteLine(simpleSentimentWithoutContrastResult);
Console.WriteLine(sentimentResult);
Console.WriteLine(sentimentAndConstrastResult);
Console.WriteLine(sentimentWithoutContrastResult);
Console.WriteLine(sentimentWithKnownTargetResult);
Console.WriteLine(sentimentAndConstrastWithKnownTargetResult);
Console.ReadLine();
}