public void CorrectUp()
{
var articlesFilePath = @"TestData\Index\MiddleTerm\Index.MiddleTerm.Up.xml";
var macroWordsLoader = new MacroWordsLoader(@"AppData\MacroWords.xml");
var patternsLoader = new RawPatternsLoader(@"AppData\Up.xml", Trend.Up);
var patternProvider = new PatternsProvider(patternsLoader.Patterns, macroWordsLoader.MacroWords);
var classifier = new OpinionClassifier(patternProvider);
var articles = new TestArticleLoader().LoadArticles(articlesFilePath);
foreach (var article in articles)
{
if (String.IsNullOrEmpty(article.Title) && String.IsNullOrEmpty(article.Text))
{
continue;
}
Trend result = classifier.Classify(article).Trend;
Assert.AreEqual(Trend.Up, result, "Incorrect classification for: " + article.Id + " : " + article.Text);
}
}
private static void Check(string testArticlesFilePath, string rawPatternsPath, Trend trend)
{
var macroWordsLoader = new MacroWordsLoader(@"AppData\MacroWords.xml");
var patternsLoader = new RawPatternsLoader(rawPatternsPath, trend);
var patternProvider = new PatternsProvider(patternsLoader.Patterns, macroWordsLoader.MacroWords);
var classifier = new OpinionClassifier(patternProvider);
var articles = new TestArticleLoader().LoadArticles(testArticlesFilePath);
foreach (var article in articles)
{
if (String.IsNullOrEmpty(article.Title) && String.IsNullOrEmpty(article.Text))
{
continue;
}
var result = classifier.Classify(article);
Assert.AreEqual(Trend.Unknown, result.Trend, "Incorrect classification: Regex: " + result.RegexText + "\n\r : " + article.Text + " \n\r " + "Regex: " + result.RegexText);
if (String.IsNullOrEmpty(article.NegativeText) == false)
{
var negativeResult = classifier.Classify(article.NegativeText);
Assert.AreNotEqual(Trend.Unknown, negativeResult, "Incorrect classification for negative text: " + article.Id + " : " + article.Text);
}
}
}
private static void CorrectDown(string testArticlesFilePath)
{
var patternsLoader = new RawPatternsLoader(@"AppData\TrendPatterns\Down.Unconditional.xml", Trend.Down);
var macroWordsLoader = new MacroWordsLoader(@"AppData\TrendPatterns\MacroWords.xml");
var patternProvider = new PatternsProvider(patternsLoader.Patterns, macroWordsLoader.MacroWords);
var classifier = new OpinionClassifier(patternProvider);
var articles = new TestArticleLoader().LoadArticles(testArticlesFilePath);
foreach (var article in articles)
{
if (String.IsNullOrEmpty(article.Title) && String.IsNullOrEmpty(article.Text))
{
continue;
}
var result = classifier.Classify(article);
Assert.AreEqual(Trend.Down, result, "Incorrect classification for: " + article.Id + " : " + article.Text);
if (String.IsNullOrEmpty(article.NegativeText) == false)
{
var negativeResult = classifier.Classify(article.NegativeText);
Assert.AreNotEqual(Trend.Down, negativeResult, "Incorrect classification for negative text: " + article.Id + " : " + article.Text);
}
}
}
public void Initial()
{
var item = new Article("", "");
var classifier = new OpinionClassifier(new PatternsProvider(new RawPattern[0], new MacroWord[0]));
Trend result = classifier.Classify(item).Trend;
Assert.AreEqual(Trend.Unknown, result);
}