//returns 1 for a positive prediciton 0 for negative prediction, and -1 if there is not enough certainty
public int makeLogPrediction(List <string> input)
{
tokenizer tokenizeObj = new tokenizer();
List <string> tokenized = input;
List <int> posInstances = new List <int>(), negInstances = new List <int>();
string tempTuple, tempTriple;
//we check each token to see if it exists in the given dictionary, if it does, add the amount of instances to the vector
//here we change 'i' depending on the start location of the text to be analyzed within the tokens
for (int i = 0; i < tokenized.Count; i++)
{
//this means we have to make a tuple
if (i < tokenized.Count - 1)
{
//this makes tempTuple a way to search the dictionary for the given tuple
tempTuple = tokenized[i] + " " + tokenized[i + 1];
if (positiveTuple.ContainsKey(tempTuple))// positiveTuple.find(tempTuple) != positiveTuple.end())
{
posInstances.Add(positiveTuple[tempTuple]);
}
else
{
posInstances.Add(1);
}
if (negativeTuple.ContainsKey(tempTuple))// negativeTuple.find(tempTuple) != negativeTuple.end())
{
negInstances.Add(negativeTuple[tempTuple]);
}
else
{
negInstances.Add(1);
}
}
//this means make a triple
if (i < tokenized.Count - 2 && tokenized.Count != 1)
{
//this makes tempTriple a way to search the dictionary for the given tuple
tempTriple = tokenized[i] + " " + tokenized[i + 1] + " " + tokenized[i + 2];
if (positiveTriple.ContainsKey(tempTriple))// positiveTriple.find(tempTriple) != positiveTriple.end())
{
posInstances.Add(positiveTriple[tempTriple]);
}
else
{
posInstances.Add(1);
}
if (negativeTriple.ContainsKey(tempTriple))// negativeTriple.find(tempTriple) != negativeTriple.end())
{
negInstances.Add(negativeTriple[tempTriple]);
}
else
{
negInstances.Add(1);
}
}
if (positiveSingle.ContainsKey(tokenized[i]))// positiveSingle.find(tokenized[i]) != positiveSingle.end())
{
posInstances.Add(positiveSingle[tokenized[i]]);
}
else
{
posInstances.Add(1);
}
if (negativeSingle.ContainsKey(tokenized[i]))// negativeSingle.find(tokenized[i]) != negativeSingle.end())
{
negInstances.Add(negativeSingle[tokenized[i]]);
}
else
{
negInstances.Add(1);
}
}
//here we get the summation of each positive instance we found divided by the total positive sentiment found
double positiveNumerator = 0;
for (int i = 0; i < posInstances.Count; i++)
{
positiveNumerator += Math.Log((double)posInstances[i] / (double)positiveSentimentTotal);
}
//here we get the summation of each negative instance we found divided by the total negative sentiment found
double negativeNumerator = 0;
for (int i = 0; i < negInstances.Count; i++)
{
negativeNumerator += Math.Log((double)negInstances[i] / (double)negativeSentimentTotal);
}
//now add the found product by the fraction of the total data points
positiveNumerator += Math.Log((double)positiveSentimentTotal / ((double)positiveSentimentTotal + (double)negativeSentimentTotal));
negativeNumerator += Math.Log((double)negativeSentimentTotal / ((double)positiveSentimentTotal + (double)negativeSentimentTotal));
if (Math.Abs(positiveNumerator - negativeNumerator) < 2)
{
return(-1);
}
//return true for a positive prediction, negative for negative prediction
if (positiveNumerator > negativeNumerator)
{
return(1);
}
else
{
return(0);
}
}
public List <bool> runPrediction(List <string> toPredict)
{
List <bool> ToRet = new List <bool>();
tokenizer t = new tokenizer();
List <string> tokens;
int retValue;
List <int> retVals = new List <int>();
List <string> badTweets = new List <string>();
foreach (var tweet in toPredict)
{
tokens = t.tokenize(tweet);
retValue = makeLogPrediction(tokens);
retVals.Add(retValue);
//if the returned value for the tweet says that we did not have sufficient evidence to make a prediction, add it to this list
if (retValue == -1)
{
badTweets.Add(tweet);
}
}
//create file for fastText to read from
var badTweetsPath = ResourceHelper.WriteToTemp("badtweets.txt", badTweets);
if (badTweets.Count > 0)
{
Console.WriteLine("Running fastText on: " + badTweets.Count.ToString() + " Tweets");
runFastText.runPrediction(badTweetsPath);
int listCount = 0;
List <int> badLabels = runFastText.getLabels();
int tester = 0;
for (int i = 0; i < retVals.Count; i++)
{
tester = 1;
try
{
tester = 2;
if (retVals[i] == -1)
{
tester = 3;
retVals[i] = badLabels[listCount]; // badLabels.count == 0. FT isn't saving right
tester = 4;
listCount++;
}
tester = 5;
if (retVals[i] == 1)
{
tester = 6;
ToRet.Add(true);
}
else
{
tester = 7;
ToRet.Add(false);
}
tester = 8;
}
catch (Exception x)
{
string message = $@"
tester={tester}
i={i}
listCount={listCount}
retVal.Count={retVals.Count}
badLabels.Count={badLabels.Count}";
throw new Exception(message, x);
}
}
}
return(ToRet);
}