Styjun

I have to Calculate Precesion, F-measure and Recall for the Naive and Svm with sentiment classification. it return's me error as string object has no attribute copy.
In code preprocessedTrainingSet gives the processed Training Data and preprocessedTestSet gives the processed test dataset

word_features = buildVocabulary(preprocessedTrainingSet)
trainingFeatures=nltk.classify.apply_features(extract_features,preprocessedTrainingSet)

NBayesClassifier=nltk.NaiveBayesClassifier.train(trainingFeatures)
accuracy = nltk.classify.util.accuracy(NBayesClassifier, preprocessedTestSet) #this returns error

I am posting my whole code here:

import csv
import re
from nltk.tokenize import word_tokenize
from string import punctuation
from nltk.corpus import stopwords
import nltk 
import sys
import os
nltk.download('punkt')
import csv
import datetime
from bs4 import BeautifulSoup
import re
import itertools
import emoji

def load_dict_smileys():

 return 
 ":‑)":"smiley",
 ":-]":"smiley",

 


def load_dict_contractions():

 return 
 "ain't":"is not",
 "amn't":"am not",

 


def strip_accents(text):
 if 'ø' in text or 'Ø' in text:
 #Do nothing when finding ø 
 return text 
 text = text.encode('ascii', 'ignore')
 text = text.decode("utf-8")
 return str(text)



def buildTestSet():
 Test_data = []
 for line in open('Avengers.csv','r'):
 cells = line.split( "," )
 Test_data.append(cells[1])

 return Test_data


testData = buildTestSet()


def buildTrainingSet(corpusFile):
 corpus = []
 trainingDataSet = []
 with open(corpusFile, "rt", encoding="utf8") as csvFile:
 lineReader = csv.reader(csvFile,delimiter=',', quotechar=""")

 for row in lineReader:
 trainingDataSet.append(row)
 return trainingDataSet

corpusFile = "trainingSet.csv"
trainingData = buildTrainingSet(corpusFile)


class PreProcessTweets:
 def __init__(self):
 self._stopwords = set(stopwords.words('english') + list(punctuation) + ['AT_USER','URL'])

 def processTweets(self, list_of_tweets):
 processedTweets=[]
 for tweet in list_of_tweets:

 if testD == 1:
 #print(tweet)
 processedTweets.append((self._processTweet(tweet),tweet[3]))
 else:
 processedTweets.append((self._processTweet(tweet[2]),tweet[3]))

 return processedTweets

 def _processTweet(self, tweet):
 tweet = BeautifulSoup(tweet).get_text()
 tweet = tweet.replace('x92',"'")
 tweet = ' '.join(re.sub("(@[A-Za-z0-9]+)|(#[A-Za-z0-9]+)", " ", tweet).split())
 tweet = ' '.join(re.sub("(w+://S+)", " ", tweet).split())
 tweet = ' '.join(re.sub("[.,!?:;-=]", " ", tweet).split())
 #Lower case
 tweet = tweet.lower()
 CONTRACTIONS = load_dict_contractions()
 tweet = tweet.replace("’","'")
 words = tweet.split()
 reformed = [CONTRACTIONS[word] if word in CONTRACTIONS else word for word in words]
 tweet = " ".join(reformed)
 tweet = ''.join(''.join(s)[:2] for _, s in itertools.groupby(tweet))

 SMILEY = load_dict_smileys() 
 words = tweet.split()
 reformed = [SMILEY[word] if word in SMILEY else word for word in words]
 tweet = " ".join(reformed)
 #Deal with emojis
 tweet = emoji.demojize(tweet)
 #Strip accents
 tweet= strip_accents(tweet)
 tweet = tweet.replace(":"," ")
 tweet = ' '.join(tweet.split())

 return tweet

testD = 0
tweetProcessor = PreProcessTweets()
preprocessedTrainingSet = tweetProcessor.processTweets(trainingData)
testD = 1
preprocessedTestSet = tweetProcessor.processTweets(testData)




def buildVocabulary(preprocessedTrainingData):
 all_words = []

 for (words, sentiment) in preprocessedTrainingData:
 all_words.extend(words)

 wordlist = nltk.FreqDist(all_words)
 word_features = wordlist.keys()

 return word_features

def extract_features(tweet):
 tweet_words=set(tweet)
 features=
 for word in word_features:
 features['contains(%s)' % word]=(word in tweet_words)
 return features 

trainingFeatures=nltk.classify.apply_features(extract_features,preprocessedTrainingSet)

NBayesClassifier=nltk.NaiveBayesClassifier.train(trainingFeatures)



NBResultLabels = [NBayesClassifier.classify(extract_features(tweet[0])) for tweet in preprocessedTestSet]

if NBResultLabels.count('positive') > NBResultLabels.count('negative'):
 print("Overall Positive Sentiment")
 print("Positive Sentiment Percentage = " + str(100*NBResultLabels.count('positive')/len(NBResultLabels)) + "%")
else: 
 print("Overall Negative Sentiment")
 print("Negative Sentiment Percentage = " + str(100*NBResultLabels.count('negative')/len(NBResultLabels)) + "%")

accuracy = nltk.classify.util.accuracy(NBayesClassifier, preprocessedTestSet)
print(accuracy*100)

the result should come in this way

 precision recall f1-score support

 0 0.65 1.00 0.79 17
 1 0.57 0.75 0.65 16
 2 0.33 0.06 0.10 17
avg / total 0.52 0.60 0.51 50