public static SentimentAnalysisData AnalyzeReview(ReviewData data)
{
SentimentAnalysisData result = new SentimentAnalysisData {
Review = data
};
result.SentimentEvaluation = int.Parse(classifier.Classify(result.Review.reviewText));
return(result);
}
public static SentimentAnalysisData AnalyzeReview(ReviewData data)
{
SentimentAnalysisData result = new SentimentAnalysisData {
Review = data
};
result.SentimentEvaluation = regression.Transform(CalculateProbabilities(data.reviewText));
return(result);
}
public static SentimentAnalysisData AnalyzeReview(ReviewData data)
{
SentimentAnalysisData result = new SentimentAnalysisData {
Review = data
};
var newText = TextClassificationProblemBuilder.CreateNode(data.reviewText, DataHandler.Vocabulary);
result.SentimentEvaluation = model.Predict(newText);
return(result);
}
public static SentimentAnalysisData AnalyzeReview(ReviewData data)
{
SentimentAnalysisData result = new SentimentAnalysisData();
result.Review = data;
var review = data.reviewText.ToLower().Split(' ');
List <decimal> resultValues = new List <decimal>();
int positiveCount = 0;
int negativeCount = 0;
//bigrams
for (int i = 0; i < review.Length - 1; i++)
{
if (review[i] != "" && review[i + 1] != "")
{
var bigram = review[i] + " " + review[i + 1];
if (DataHandler.Bigrams.Keys.Contains(bigram))
{
decimal value = DataHandler.Bigrams[bigram];
if (value > 0)
{
positiveCount++;
}
else if (value < 0)
{
negativeCount++;
}
resultValues.Add(value);
review[i] = "";
review[i + 1] = "";
}
}
}
for (int i = 0; i < review.Count(); i++)
{
decimal multiplier = 1;
decimal value;
bool shouldNegate = false;
//negation
//if (DataHandler.NegationWords.Contains(review[i]))
//{
// shouldNegate = true;
// //Search for the next word that has some sentiment and negate it
// if (DataHandler.Lexicon.Keys.Contains(review[i+1]))
// {
// i++;
// }
// else if (DataHandler.Lexicon.Keys.Contains(review[i + 2]))
// {
// i += 2;
// }
//}
//enchancement
if (review[i] == "")
{
continue;
}
if (DataHandler.Intensifiers.Keys.Contains(review[i]))
{
multiplier += DataHandler.Intensifiers[review[i]];
i++;
}
if (i >= review.Length)
{
break;
}
//regular parsing
//TODO: think about a bigger lexicon
if ((DataHandler.Lexicon.Keys.Contains(review[i])) == false)
{
continue;
}
if (shouldNegate)
{
value = (multiplier * (ApplyNegativeCalculationOnWord(review[i])));
}
else
{
value = (multiplier * (DataHandler.Lexicon[review[i]]));
}
if (value > 0)
{
positiveCount++;
}
else if (value < 0)
{
negativeCount++;
}
resultValues.Add(value);
}
result.PositiveWordsCount = positiveCount;
result.NegativeWordsCount = negativeCount;
var totalEvaluated = positiveCount + negativeCount;
if (totalEvaluated > 0)
{
result.SentimentEvaluation = (double)CalculateOveralSentiment(resultValues, positiveCount, negativeCount, review.Length);
}
else
{
result.SentimentEvaluation = 0;
}
return(result);
}