public int Predict(string input)
{
var newX = TextClassificationProblemBuilder.CreateNode(input, vocabulary);
var predictedY = model.Predict(newX);
return((int)predictedY);
}
private SvmMethod()
{
var path = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "Files/SentimentAnalysisDataset.csv");
List <string> x = new List <string>();
List <double> y = new List <double>();
if (File.Exists(path))
{
var lines = File.ReadAllLines(path);
for (int i = 0; i < 500; i++)//5146
{
var lineArr = lines[i].Split(new string[] { ",Sentiment140,", ",Kaggle," }, StringSplitOptions.None);
y.Add(double.Parse(lineArr[0].Split(',')[1]));
x.Add(lineArr[1].Trim());
}
}
//var dataTable = DataTable.New.ReadCsv(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "Files/spamdata.csv"));
//List<string> x = dataTable.Rows.Select(row => row["Text"]).ToList();
//double[] y = dataTable.Rows.Select(row => double.Parse(row["IsSpam"])).ToArray();
vocabulary = x.SelectMany(GetWords).Distinct().OrderBy(word => word).ToList();
var problemBuilder = new TextClassificationProblemBuilder();
var problem = problemBuilder.CreateProblem(x, y.ToArray(), vocabulary.ToList());
const int C = 1;
model = new C_SVC(problem, KernelHelper.LinearKernel(), C);
_predictionDictionary = new Dictionary <int, string> {
{ 0, "negative" }, { 1, "positive" }
};
}
public void Create_Train_SVMmodel(string path_dataCSV_trainning, double C)
{
var dataTable = DataAccess.DataTable.New.ReadCsv(path_dataCSV_trainning);
List <string> x = dataTable.Rows.Select(row => row["text"]).ToList();
double[] y = dataTable.Rows.Select(row => double.Parse(row["class"])).ToArray();
vocabulary = x.SelectMany(GetWords).Distinct().OrderBy(word => word).ToList();
var problemBuilder = new TextClassificationProblemBuilder();
var problem = problemBuilder.CreateProblem(x, y.ToArray(), vocabulary.ToList());
model = new C_SVC(problem, KernelHelper.LinearKernel(), C);
}
public int Analyze(string message)
{
//var accuracy = model.GetCrossValidationAccuracy(10);
//var path = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, string.Format("Files/model_{0}_accuracy.model", accuracy));
//model.Export(path);
var newX = TextClassificationProblemBuilder.CreateNode(message, vocabulary);
var predictedY = model.Predict(newX);
var predictedProb = model.PredictProbabilities(newX);
//return _predictionDictionary[(int)predictedY];
return((int)predictedY > 0 ? 1 : -1);
}