public TTweet AnalyzeSingle(ITweet tweet)
{
TTweet temp = new TTweet();
temp.t = tweet;
string[] words = tweet.Text.Split(' ');
temp.numWords = words.Length;
//Pulls out 6 emotionalChar of a tweet
pullSixOut(words, temp);
pullSubjectiviityOut(words, temp);
return(temp);
}
public void pullSubjectiviityOut(string[] words, TTweet temp)
{
temp.subjectivities = new List <TTweet.subjectivity>();
foreach (var tweet_word in words)
{
foreach (var word_dic in wordSbs)
{
if (tweet_word.Contains(word_dic.word))
{
temp.pos = (word_dic.posneg.Contains("positive")) ? TTweet.positive.POSITIVE : TTweet.positive.NEGATIVE;
temp.str = (word_dic.posneg.Contains("strongsubj")) ? TTweet.strong.STRONG : TTweet.strong.WEAK;
TTweet.subjectivity tempSub = new TTweet.subjectivity(true, true);
if (temp.pos == TTweet.positive.POSITIVE)
{
if (temp.str == TTweet.strong.STRONG)
{
tempSub.positive = true;
tempSub.strong = true;
}
else if (temp.str == TTweet.strong.WEAK)
{
tempSub.positive = true;
tempSub.strong = false;
}
}
else if (temp.pos == TTweet.positive.NEGATIVE)
{
if (temp.str == TTweet.strong.STRONG)
{
tempSub.positive = false;
tempSub.strong = true;
}
else if (temp.str == TTweet.strong.WEAK)
{
tempSub.positive = false;
tempSub.strong = false;
}
}
temp.subjectivities.Add(tempSub);
break;
}
}
}
}
//Updates the tweets score of the 6 traits based on emotions
public void pullSixOut(string[] words, TTweet temp)
{
foreach (var word in words)
{
countWord(word);
foreach (var emotion in wordEs)
{
if (word.Contains(emotion.e))
{
switch (emotion.word)
{
case "joy":
temp.joy++;
break;
case "anger":
temp.anger++;
break;
case "surprise":
temp.surprise++;
break;
case "disgust":
temp.disgust++;
break;
case "fear":
temp.fear++;
break;
case "sadness":
temp.sadness++;
break;
default:
break;
}
break;
}
}
}
}
private TTweetList AnalyzeList(IEnumerable <ITweet> tweets)
{
var tempList = new TTweetList();
var temp = new List <TTweet>();
int tweetCounter = 0;
int wordCounter = 0;
double tempPos = 0;
double tempNeg = 0;
foreach (var item in tweets)
{
//Need to account for word and text lenght missing from TTWEET
TTweet f = AnalyzeSingle(item);
temp.Add(f);
tempPos += f.getSubHits(TTweet.positive.POSITIVE, TTweet.strong.ALL);
tempNeg += f.getSubHits(TTweet.positive.NEGATIVE, TTweet.strong.ALL);
wordCounter += f.numWords;
tempList.anger += f.anger;
tempList.disgust += f.disgust;
tempList.fear += f.fear;
tempList.joy += f.joy;
tempList.surprise += f.surprise;
tempList.sadness += f.sadness;
tweetCounter++;
}
if (tweetCounter != 0)
{
tempList.tweetLength = wordCounter / tweetCounter;
tempPos = tempPos / tweetCounter;
tempNeg = tempNeg / tweetCounter;
}
double c = 35 + ((tempPos - tempNeg * .5) / (tempPos + tempNeg * .5) * 500);
if (tempPos == 0 && tempNeg == 0)
{
c = 0;
}
Console.WriteLine(tempPos + "||" + tempNeg);
c = (c >= 100) ? 100 : c;
c = (c <= -100) ? -100 : c;
tempList.positivity = c;
tempList.size =
tempList.anger +
tempList.disgust +
tempList.fear +
tempList.joy +
tempList.surprise +
tempList.sadness;
wordCountList = wordCountList.OrderByDescending(x => x.Value).ToDictionary(x => x.Key, x => x.Value);
topN = new List <string>();
int countt = 0;
string line;
foreach (var item in wordCountList)
{
StreamReader file = new System.IO.StreamReader("DataClassify/NounList.csv");
while ((line = file.ReadLine()) != null)
{
if (item.Key.Equals(line) && countt < 10)
{
topN.Add(item.Key);
countt++;
if (countt > 10)
{
return(tempList);
}
}
}
}
return(tempList);
}
