public ActionResult ClassifyUnknown(FormCollection collection)
{
DataTable table = new DataTable();
table.Columns.Add("Sex");
table.Columns.Add("Height", typeof(double));
table.Columns.Add("Weight", typeof(double));
table.Columns.Add("Age", typeof(double));
//training data.
for (int i = 0; i < trainingData.maleList.Count; i++)
{
table.Rows.Add("male", trainingData.maleList[i].height, trainingData.maleList[i].weight, Convert.ToDouble(trainingData.maleList[i].age));
}
for (int i = 0; i < trainingData.femaleList.Count; i++)
{
table.Rows.Add("female", trainingData.femaleList[i].height, trainingData.femaleList[i].weight, Convert.ToDouble(trainingData.femaleList[i].age));
}
//classify
Classifier classifier = new Classifier();
classifier.TrainClassifier(table);
string result = "";
//result = classifier.Classify(new double[] { trainingData.unknownList.Last().height, trainingData.unknownList.Last().weight, Convert.ToDouble(trainingData.unknownList.Last().age) });
//DatabaseHelper.DeleteClassifiedUnknown(trainingData.unknownList.Last().unknownId);
for (int i = 0; i < trainingData.unknownList.Count; i++)
{
result = classifier.Classify(new double[] { trainingData.unknownList[i].height, trainingData.unknownList[i].weight, Convert.ToDouble(trainingData.unknownList[i].age) });
switch (result)
{
case "male":
table.Rows.Add("male", trainingData.unknownList[i].height, trainingData.unknownList[i].weight, trainingData.unknownList[i].age);
DatabaseHelper.addNewMale(trainingData.unknownList[i].weight, trainingData.unknownList[i].height, trainingData.unknownList[i].age);
DatabaseHelper.DeleteClassifiedUnknown(trainingData.unknownList[i].unknownId);
break;
case "female":
table.Rows.Add("female", trainingData.unknownList[i].height, trainingData.unknownList[i].weight, trainingData.unknownList[i].age);
DatabaseHelper.addNewFemale(trainingData.unknownList[i].weight, trainingData.unknownList[i].height, trainingData.unknownList[i].age);
DatabaseHelper.DeleteClassifiedUnknown(trainingData.unknownList[i].unknownId);
break;
default:
break;
}
}
return Redirect("/");
}
static void OpinionGram(string TERM, string tweetFilePath, string reportFilePath, string reportXMLpath, int dataSize)
{
NGramClassifier NGC = new NGramClassifier();
NGC.Load("NGC.csv");
Classifier POSC = new Classifier();
POSC.load("sentiobj.csv");
initPOSIndex();
StreamWriter fs = new StreamWriter(reportFilePath, false);
char[] space = { ' ', '\t' };
char[] underScore = { '_' };
string[] tData = File.ReadAllLines(tweetFilePath);
if (tData.Length < dataSize)
{
dataSize = tData.Length;
}
double totalScore = 0;
double posTot = 0, negTot = 0;
int posCnt = 0, negCnt = 0;
int i;
fs.WriteLine("i" + "," + "score" + "," + "marker" + "," + "ngcScore" + "," + "posScore" + "," + "sens" + "," + "tweet");
for (i = 0; i < dataSize; i++)
{
string line = tData[i];
//string[] lineParts = line.Split(space, 4);
string taggedTweet = line;
string[] tweetParts = taggedTweet.Split(" ".ToCharArray(), StringSplitOptions.RemoveEmptyEntries);
string tweet = "";
foreach (string twpart in tweetParts)
{
string word = twpart.Split("_".ToCharArray())[0];
tweet += word + " ";
}
taggedTweet = line;
double[] P = convert(posDist(taggedTweet));
double ngcScore = NGC.score(tweet);
double posScore = POSC.BinClassify(P);
string sens = POSC.Classify(P);
double score = 0.0;
double NPosScore = (posScore / 2) + 0.0;
if (NPosScore > +1)
{
NPosScore = +.85;
}
if (NPosScore < -1)
{
NPosScore = -.85;
}
score = NPosScore + ngcScore;
if (score == double.NaN)
{
int q = 0;;
}
string marker = "neutral";
if (score < -0.25)
{
marker = "negative"; negCnt++;
}
else if (score > +0.25)
{
marker = "positive"; posCnt++;
}
totalScore += score;
fs.WriteLine(i + "," + score + "," + marker + "," + ngcScore + "," + posScore + "," + sens + "," + tweet);
}
fs.Close();
Console.WriteLine("avg score = " + (totalScore / i));
Console.WriteLine("neg pct = " + (((double)negCnt) / i));
Console.WriteLine("pos pct = " + (((double)posCnt) / i));
Console.WriteLine("post count= " + i);
StreamWriter XMLfs = new StreamWriter(reportXMLpath, false);
XMLfs.WriteLine("<opinion entity='" + TERM + "'>");
XMLfs.WriteLine(" <score>" + (totalScore / i).ToString("F2") + "</score>");
XMLfs.WriteLine(" <analysis ");
XMLfs.WriteLine(" post-count='" + i + "'");
XMLfs.WriteLine(" percent-positive='" + (((double)posCnt) * 100 / i).ToString("F2") + "'");
XMLfs.WriteLine(" percent-negative='" + (((double)negCnt) * 100 / i).ToString("F2") + "'" + " />");
XMLfs.WriteLine("</opinion>");
XMLfs.Close();
}
static void Test(string testFilePath, string reportFilePath, int dataSize)
{
Classifier classifier = new Classifier();
classifier.load("senti.csv");
Classifier objOpinCl = new Classifier();
objOpinCl.load("sentiobj.csv");
StreamWriter fs = new StreamWriter(reportFilePath, false);
initPOSIndex();
char[] space = { ' ' };
char[] underScore = { '_' };
string[] tData = File.ReadAllLines(testFilePath);
//string[] joys = File.ReadAllLines(happyFilePath);
if (tData.Length < dataSize)
{
dataSize = tData.Length;
}
double total = 0;
int n = 0;
int err = 0, ers = 0, ero = 0;
double posTot = 0, negTot = 0;
int posCnt = 0, negCnt = 0;
int realPos = 0;
int realNut = 0;
for (int i = 1; i < dataSize; i++)
{
string line = tData[i];
string[] lineParts = line.Split(space, 4);
string tweet = lineParts[3];
string tag = lineParts[0];
tag = tag.Split(underScore, 2)[0];
double[] P = convert(posDist(tweet));
double sent = classifier.BinClassify(P);
string sens = objOpinCl.Classify(P);
double objSent = objOpinCl.BinClassify(P);
Console.WriteLine(tag + " " + sent + " " + sens);
/*if (tag != "neutral")
* {
* if (tag != sens) ers++;
* }*/
if (sens == "objective")
{
sens = "neutral";
}
if (tag != sens)
{
ers++;
}
if (tag == "positive")
{
realPos++;
}
if (tag == "neutral")
{
realNut++;
}
if (sent > 0)
{
posTot += sent;
posCnt++;
}
else
{
negTot += sent;
negCnt++;
}
string marker = "neutral";
if (sent < -1.2)
{
marker = "negative";
}
else if (sent > 1.01)
{
marker = "positive";
}
string ObSeMarker = "neutral";
if (objSent < -1.2)
{
ObSeMarker = "negative";
}
else if (objSent > 1.01)
{
ObSeMarker = "positive";
}
fs.WriteLine(i + "," + tag + "," + sent + "," + objSent + "," + sens + "," + tweet);
if (tag != marker)
{
err++;
}
if (tag != ObSeMarker)
{
ero++;
}
}
fs.Close();
Console.WriteLine("s err: " + ers + " b err:" + err + " obse err:" + ero);
}
static void BothTest(string testFilePath, string reportFilePath, int dataSize)
{
NGramClassifier NGC = new NGramClassifier();
NGC.Load("NGC.csv");
Classifier POSC = new Classifier();
POSC.load("sentiobj.csv");
initPOSIndex();
StreamWriter fs = new StreamWriter(reportFilePath, false);
char[] space = { ' ', '\t' };
char[] underScore = { '_' };
string[] tData = File.ReadAllLines(testFilePath);
if (tData.Length < dataSize)
{
dataSize = tData.Length;
}
double total = 0;
int n = 0;
int err = 0;
int berr = 0;
int btot = 0;
double posTot = 0, negTot = 0;
int posCnt = 0, negCnt = 0;
int realPos = 0;
int realNut = 0;
fs.WriteLine("i" + "," + "tag" + "," + "score" + "," + "marker" + "," + "ngcScore" + "," + "posScore" + "," + "sens" + "," + "score" + "," + "tweet");
for (int i = 1; i < dataSize; i++)
{
string line = tData[i];
string[] lineParts = line.Split(space, 4);
string taggedTweet = lineParts[3];
string[] tweetParts = taggedTweet.Split(" ".ToCharArray(), StringSplitOptions.RemoveEmptyEntries);
string tweet = "";
foreach (string twpart in tweetParts)
{
string word = twpart.Split("_".ToCharArray())[0];
tweet += word + " ";
}
taggedTweet = lineParts[3];
string tag = lineParts[0];
tag = tag.Split(underScore, 2)[0];
if (tag != "neutral")
{
int x = 0;
}
double[] P = convert(posDist(taggedTweet));
double ngcScore = NGC.score(tweet);
double posScore = POSC.BinClassify(P);
string sens = POSC.Classify(P);
if (tag == "positive")
{
realPos++;
}
if (tag == "neutral")
{
realNut++;
}
double score = 0.0;
double NPosScore = (posScore / 2) + .23;
if (NPosScore > +1)
{
NPosScore = +.71;
}
if (NPosScore < -1)
{
NPosScore = -.6;
}
score = NPosScore + ngcScore;
if (score > 0)
{
posTot += score;
posCnt++;
}
else
{
negTot += score;
negCnt++;
}
string marker = "neutral";
if (score < -0.5)
{
marker = "negative";
}
else if (score > +0.5)
{
marker = "positive";
}
if (tag != marker)
{
err++;
}
if (tag != "neutral" && marker != "neutral")
{
btot++;
if (tag != marker)
{
berr++;
}
}
fs.WriteLine(i + "," + tag + "," + score + "," + marker + "," + ngcScore + "," + posScore + "," + sens + "," + tweet);
}
fs.Close();
Console.WriteLine("err: " + err);
Console.WriteLine("real biased: " + btot);
Console.WriteLine("err on biased: " + berr);
Console.WriteLine("err on biased ratio: " + ((double)berr / btot));
}
//Entity,KeyWords, Opiniongram Scores
public void TEkwP(string tweetFilePath, string reportFilePath, int dataSize)
{
DateTime startTime = DateTime.Now;
NGramClassifier NGC = new NGramClassifier();
NGC.Load("NGC.csv");
Classifier POSC = new Classifier();
POSC.load("sentiobj.csv");
Program.initPOSIndex();
StreamWriter fs = new StreamWriter(reportFilePath + ".csv", false);
StreamWriter xfs = new StreamWriter(reportFilePath + ".xml", false);
char[] space = { ' ', '\t' };
char[] underScore = { '_' };
string[] tData = File.ReadAllLines(tweetFilePath);
if (tData.Length < dataSize || dataSize == -1)
{
dataSize = tData.Length;
}
double totalScore = 0;
int posCnt = 0, negCnt = 0;
int i;
fs.WriteLine("i" + "," + "Tweet" + "," + "E" + "," + "kw" + "," + "Polarity Score" + "," + "marker" + "," + "ngcScore" + "," + "posScore" + "," + "sens");
xfs.WriteLine("<TEkwPs>");
Console.WriteLine("TEkwP");
Console.WriteLine("take up: lyzing tweets");
for (i = 0; i < dataSize; i++)
{
string line = tData[i];
string E = "";
string kw = "";
if (i % 100 == 0)
{
Console.Write("\r" + i);
}
//string[] lineParts = line.Split(space, 4);
string taggedTweet = line;
string[] tweetParts = taggedTweet.Split(" ".ToCharArray(), StringSplitOptions.RemoveEmptyEntries);
string tweet = "";
int ti = 0;
int prevEntityIndex = -999;
string prevEntity = "";
foreach (string twpart in tweetParts)
{
string word = twpart.Split("_".ToCharArray())[0];
string pos = twpart.Split("_".ToCharArray())[1];
tweet += word + " ";
word = CleanWord(word);
if (isKeyWord(pos, word))
{
kw += keyWordiFy(word) + ";";
}
if (isEntity(pos, word))
{
string bs = "";
if ((prevEntityIndex + 1 == ti) && !ContainFilter(prevEntity))
{
bs = "<:< ";
}
E += bs + EntitiFy(word) + ";";
prevEntityIndex = ti;
prevEntity = word;
}
ti++;
}
E = E.Replace(";<:< ", " ");
taggedTweet = line;
double[] P = Program.convert(Program.posDist(taggedTweet));
double ngcScore = NGC.score(tweet);
double posScore = POSC.BinClassify(P);
string sens = POSC.Classify(P);
double pScore = 0.0;
double NPosScore = (posScore / 2) + 0.20;
if (NPosScore > +1)
{
NPosScore = +.85;
}
if (NPosScore < -1)
{
NPosScore = -.85;
}
pScore = NPosScore + ngcScore;
if (pScore == double.NaN)
{
int q = 0;;
}
string marker = "neutral";
if (pScore <= -0.45)
{
marker = "negative"; negCnt++;
}
else if (pScore >= +0.45)
{
marker = "positive"; posCnt++;
}
totalScore += pScore;
fs.WriteLine(i + ",\"" + tweet.Replace(",", "_CM_") + "\"," + E + "," + kw + "," + pScore + "," + marker + "," + ngcScore + "," + posScore + "," + sens);
xfs.WriteLine("<TEkwP i='" + i + "' pScore='" + pScore + "' marker='" + marker + "'>");
xfs.WriteLine(" <T>" + WebUtility.HtmlEncode(tweet.Replace("_CM_", ",")) + "</T>");
xfs.WriteLine(" <E>" + WebUtility.HtmlEncode(E.Replace(";", ",")) + "</E>");
xfs.WriteLine(" <kw>" + WebUtility.HtmlEncode(kw.Replace(";", ",")) + "</kw>");
xfs.WriteLine("</TEkwP>");
}
xfs.WriteLine("</TEkwPs>");
fs.Close();
xfs.Close();
DateTime endTime = DateTime.Now;
TimeSpan ts = endTime - startTime;
Console.WriteLine("\r" + dataSize + "\ndone! in " + ts + "\nStored TEkwP file at " + reportFilePath + ".csv \n\tand at " + reportFilePath + ".xml");
XmlDocument xDoc = new XmlDocument();
xDoc.Load(reportFilePath + ".xml");
xDoc.Normalize();
xDoc.Save(reportFilePath + ".xml");
}