def __init__(self):

self.query_list = {

"anger": r"D:\Thesis\Thesis-CE\Phyton Program\QueryTerms\anger.txt",

"joy": r"D:\Thesis\Thesis-CE\Phyton Program\QueryTerms\joy.txt",

"fear": r"D:\Thesis\Thesis-CE\Phyton Program\QueryTerms\fear.txt",

"sadness": r"D:\Thesis\Thesis-CE\Phyton Program\QueryTerms\sadness.txt"

}

self.queryDict = dict()

def fit(self, emotion: str):

with open(self.query_list[emotion], 'r', errors="surrogateescape") as file:

for line in file:

self.queryDict[line.strip('\n')] = []

def labelQueryTerm(self, tweetsList):

tokenizer = Tokenizer.Tokenizer()

for tweet in tweetsList:

termsInTweets = tokenizer.tokenize(tweet[2], 'simple')

for term in termsInTweets:

if term in list(self.queryDict.keys()):

self.queryDict[term].append(tweet[1])

self._scoreQueryTerms()

def getScore(self,tokenizedTweet, noOfClasses=4): # in our work each tweet might be from one of the four possible classes

scores = numpy.zeros(noOfClasses)

for term in tokenizedTweet:

if term in list(self.queryDict.keys()):

for index, score in enumerate(self.queryDict[term]):

scores[index] = max(scores[index], score)

return scores.tolist()

def _scoreQueryTerms(self):

for term in list(self.queryDict.keys()):

labelList = numpy.array(self.queryDict[term])

unique, counts = numpy.unique(labelList, return_counts=True)