Getting for line in plaintext.split('\n'): UnicodeDecodeError: 'ascii' codec can't decode byte 0x96 in position 2: ordinal not in range(128)
<p>I'm going to design sentimental analysis on twitter data using nltk tutorials but not able to run following code</p>

<pre><code>import pickle
import random

import nltk
from nltk import pos_tag
from nltk.classify import ClassifierI
from nltk.classify.scikitlearn import SklearnClassifier
from nltk.tokenize import word_tokenize
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.svm import LinearSVC
from statistics import mode


class VoteClassifier(ClassifierI):
    def __init__(self, *classifiers):
        self._classifiers = classifiers

    def classify(self, features):
        votes = []
        for c in self._classifiers:
            v = c.classify(features)
            votes.append(v)
        return mode(votes)

    def confidence(self, features):
        votes = []
        for c in self._classifiers:
            v = c.classify(features)
            votes.append(v)

        choice_votes = votes.count(mode(votes))
        conf = choice_votes / len(votes)
        return conf


short_pos = open("positive.txt", "r").read()

short_neg = open("negative.txt", "r").read()

# move this up here
all_words = []
documents = []

#  j is adject, r is adverb, and v is verb
# allowed_word_types = ["J","R","V"]
allowed_word_types = ["J"]

for p in short_pos.split('\n'):
    documents.append((p, "pos"))
    words = word_tokenize(p)
    pos = pos_tag(words)
    for w in pos:
        if w[1][0] in allowed_word_types:
            all_words.append(w[0].lower())

for p in short_neg.split('\n'):
    documents.append((p, "neg"))
    words = word_tokenize(p)
    pos = pos_tag(words)
    for w in pos:
        if w[1][0] in allowed_word_types:
            all_words.append(w[0].lower())

save_documents = open("pickled_algos/documents.pickle", "wb")
pickle.dump(documents, save_documents)
save_documents.close()

all_words = nltk.FreqDist(all_words)

word_features = list(all_words.keys())[:5000]

save_word_features = open("pickled_algos/word_features5k.pickle", "wb")
pickle.dump(word_features, save_word_features)
save_word_features.close()


def find_features(document):
    words = word_tokenize(document)
    features = {}
    for w in word_features:
        features[w] = (w in words)

    return features


featuresets = [(find_features(rev), category) for (rev, category) in documents]

random.shuffle(featuresets)
print(len(featuresets))

testing_set = featuresets[10000:]
training_set = featuresets[:10000]

classifier = nltk.NaiveBayesClassifier.train(training_set)
print("Original Naive Bayes Algo accuracy percent:", (nltk.classify.accuracy(classifier, testing_set)) * 100)
classifier.show_most_informative_features(15)

###############
save_classifier = open("pickled_algos/originalnaivebayes5k.pickle", "wb")
pickle.dump(classifier, save_classifier)
save_classifier.close()

MNB_classifier = SklearnClassifier(MultinomialNB())
MNB_classifier.train(training_set)
print("MNB_classifier accuracy percent:", (nltk.classify.accuracy(MNB_classifier, testing_set)) * 100)

save_classifier = open("pickled_algos/MNB_classifier5k.pickle", "wb")
pickle.dump(MNB_classifier, save_classifier)
save_classifier.close()

BernoulliNB_classifier = SklearnClassifier(BernoulliNB())
BernoulliNB_classifier.train(training_set)
print("BernoulliNB_classifier accuracy percent:", (nltk.classify.accuracy(BernoulliNB_classifier, testing_set)) * 100)

save_classifier = open("pickled_algos/BernoulliNB_classifier5k.pickle", "wb")
pickle.dump(BernoulliNB_classifier, save_classifier)
save_classifier.close()

LogisticRegression_classifier = SklearnClassifier(LogisticRegression())
LogisticRegression_classifier.train(training_set)
print("LogisticRegression_classifier accuracy percent:",
      (nltk.classify.accuracy(LogisticRegression_classifier, testing_set)) * 100)

save_classifier = open("pickled_algos/LogisticRegression_classifier5k.pickle", "wb")
pickle.dump(LogisticRegression_classifier, save_classifier)
save_classifier.close()

LinearSVC_classifier = SklearnClassifier(LinearSVC())
LinearSVC_classifier.train(training_set)
print("LinearSVC_classifier accuracy percent:", (nltk.classify.accuracy(LinearSVC_classifier, testing_set)) * 100)

save_classifier = open("pickled_algos/LinearSVC_classifier5k.pickle", "wb")
pickle.dump(LinearSVC_classifier, save_classifier)
save_classifier.close()

# NuSVC_classifier = SklearnClassifier(NuSVC())
# NuSVC_classifier.train(training_set)
# print("NuSVC_classifier accuracy percent:", (nltk.classify.accuracy(NuSVC_classifier, testing_set))*100)

SGDC_classifier = SklearnClassifier(SGDClassifier())
SGDC_classifier.train(training_set)
print("SGDClassifier accuracy percent:", nltk.classify.accuracy(SGDC_classifier, testing_set) * 100)

save_classifier = open("pickled_algos/SGDC_classifier5k.pickle", "wb")
pickle.dump(SGDC_classifier, save_classifier)
save_classifier.close()
</code></pre>

<p>Open the files in text mode with the right encoding, e.g.:</p>

<pre><code>with io.open("positive.txt", "r", encoding="UTF8") as fd:
    short_pos = fd.read()
</code></pre>

python python-2.7 python-3.x nltk nltk-trainer