File size: 10,299 Bytes
48f3dfc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 |
# -*- coding: utf-8 -*-
"""SafaricomProject.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Q0IBBWS6EJsk7j1mGoRghqpR-dePQ3yi
"""
import pip
# pip install pandas
import numpy as np
import pandas as pd
from pip._internal.operations.install.legacy import install
# Read csv file into a pandas dataframe
# from google.colab import files
# uploaded = files.upload()
import emoji
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import string
import matplotlib.pyplot as plt
import re
#from wordcloud import WordCloud
from collections import Counter
from sklearn.cluster import KMeans
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import TfidfVectorizer
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
# Reading Dataset
df = pd.read_csv('safaricomDataset.csv')
df.head()
df.columns
df.shape
tweets_df = df[["Date", "User", "Tweet"]]
tweets_df.head()
# from sklearn import utils
tweets_df.shape
"""#Preprocessing and Cleaning of the Dataset """
nltk.download('punkt')
# pip install emoji
# import re
# import emoji
def tokenize_tweets(text):
# remove emojis
text = emoji.demojize(text)
# remove urls
text = re.sub('http[s]?://\S+', '', text)
# remove punctuations
text = re.sub(r'[^\w\s]', '', text)
# strip numbers
text = re.sub('[0-9]+', '', text)
text = word_tokenize(text)
return text
tweets_df["Tweets"] = tweets_df["Tweet"].apply(lambda x: tokenize_tweets(x))
nltk.download('stopwords')
stop = stopwords.words("english")
tweets_df["stop_words"] = tweets_df["Tweets"].apply(lambda x: [w for w in x if w in stop])
tweets_df["Tweets"] = tweets_df["Tweets"].apply(lambda x: [w.lower() for w in x if w not in stop])
tweets_df.head(10)
tweets_df.head()
string.punctuation
from nltk.stem.porter import *
stemmer = PorterStemmer()
tweets_df["Tweets"] = tweets_df["Tweets"].apply(lambda x: [stemmer.stem(w) for w in x])
tweets_df.head()
def remove_punct(text):
text = " ".join([char for char in text if char not in string.punctuation])
text = re.sub('[0-9]+', '', text)
return text
tweets_df['tweet_punct'] = tweets_df['Tweets'].apply(lambda x: remove_punct(x))
tweets_df.head()
"""#Data Visualization(Word Cloud)"""
#all_words = ' '.join([text for text in df['Tweet']])
#wordcloud = WordCloud(width=800, height=500, random_state=21, max_font_size=110).generate(all_words)
#plt.figure(figsize=(10, 7))
#plt.imshow(wordcloud, interpolation="bilinear")
#plt.axis('off')
#plt.show()
"""#Get the most frequent words"""
cnt = Counter()
for text in df["Tweet"].values:
for word in text.split():
cnt[word] += 1
cnt.most_common(20)
"""#Using Vader Library to analyse sentiments in Text"""
# !pip install vaderSentiment
"""#Training of Dataset"""
analyzer = SentimentIntensityAnalyzer()
"""#Getting the sentiments label"""
def sentiment_score_compound(sentence):
score = analyzer.polarity_scores(sentence)
return score['compound']
def sentiment_score_pos(sentence):
score = analyzer.polarity_scores(sentence)
return score['pos']
def sentiment_score_neg(sentence):
score = analyzer.polarity_scores(sentence)
return score['neg']
def sentiment_score_neu(sentence):
score = analyzer.polarity_scores(sentence)
return score['neu']
tweets_df["tweets_sent_compound"] = tweets_df["Tweet"].apply(lambda x: sentiment_score_compound(x))
tweets_df["tweets_sent_pos"] = tweets_df["Tweet"].apply(lambda x: sentiment_score_pos(x))
tweets_df["tweets_sent_neg"] = tweets_df["Tweet"].apply(lambda x: sentiment_score_neg(x))
tweets_df.head()
tweets_df.tail()
#wordlist = nltk.FreqDist(all_words)
#word_features = wordlist.keys()
"""#Vectorization"""
cv = CountVectorizer()
tweets_list = []
for tweet in tweets_df["tweet_punct"]:
tweets_list.append(tweet)
len(tweets_list)
tfIdf = TfidfVectorizer(max_features=20000)
X = tweets_df["tweet_punct"]
vec = TfidfVectorizer(min_df=5, max_df=0.95, sublinear_tf=True, use_idf=True, ngram_range=(1, 2))
#len(all_words)
"""#Define Labels(Positive, Negative, Neutral)"""
# negative label is 0
# neutral label is 1
# positive label is 2
def label_value(val):
if val < 0:
return 0
elif val == 0:
return 1
else:
return 2
tweets_df["label"] = tweets_df["tweets_sent_compound"].apply(lambda x: label_value(x))
tweets_df.head()
cv = CountVectorizer(binary=True)
cv.fit(tweets_list)
X = cv.transform(tweets_list)
y = tweets_df["label"].values
"""#Plotting the Label Results"""
# Commented out IPython magic to ensure Python compatibility.
# %matplotlib inline
plt.rcParams['figure.figsize'] = [10, 8]
for index, Tweets in enumerate(df.index):
x = tweets_df.tweets_sent_pos.loc[Tweets]
y = tweets_df.tweets_sent_neg.loc[Tweets]
plt.scatter(x, y, color='Blue')
plt.title('Safaricom Tweets Sentiment Analysis', fontsize=20)
plt.xlabel('β Negative β β β Neutral β β β Positive β', fontsize=15)
plt.ylabel('β Facts β β β β β β β Opinions β', fontsize=15)
plt.show()
"""#Plotting on a Pie Chart and Bar Chart
"""
# Commented out IPython magic to ensure Python compatibility.
# %matplotlib inline
tweets_df['label'].value_counts().plot(kind='pie', autopct='%1.0f%%')
plt.show()
tweets_df['label'].value_counts().sort_index().plot.bar()
plt.show()
"""#Classification using SVM"""
# encoder = preprocessing.LabelEncoder()
# X = tfIdf.fit_transform(df['Text'])
# y = df['tweets_sent_compound']
# X.shape
# X_train, X_test, y_train, y_test= train_test_split(X,y, test_size=0.2, random_state=0)
# encoder = preprocessing.LabelEncoder()
# y_train = encoder.fit_transform(y_train)
# y_test = encoder.fit_transform(y_test)
# X_train, X_val, y_train, y_val = train_test_split(X, y, train_size = 0.2, random_state = 0)
epochs = 20
for epoch in range(epochs):
print(f'Epochs: {epoch + 1}')
train_loss = 0
valid_loss = 0
ngram_vectorizer = CountVectorizer(binary=True, ngram_range=(1, 3))
ngram_vectorizer.fit(tweets_list)
X = ngram_vectorizer.transform(tweets_list)
y = tweets_df["label"].values
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.2, random_state=0)
svm = LinearSVC()
svm.fit(X_train, y_train)
# clf = LinearSVC()
# clf.fit(X_train, y_train)
pred = svm.predict(X_val)
print("Accuracy: ", accuracy_score(y_val, pred))
print(classification_report(y_val, pred))
print(confusion_matrix(y_val, pred))
"""#TF-IDF Vectroization"""
for epoch in range(epochs):
print(f'Epochs: {epoch + 1}')
train_loss = 0
valid_loss = 0
tfidf_vectorizer = TfidfVectorizer()
tfidf_vectorizer.fit(tweets_list)
X = tfidf_vectorizer.transform(tweets_list)
y = tweets_df["label"].values
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.2, random_state=0)
svm = LinearSVC()
svm.fit(X_train, y_train)
pred = svm.predict(X_val)
print("Accuracy: ", accuracy_score(y_val, pred))
print(classification_report(y_val, pred))
print(confusion_matrix(y_val, pred))
#print(pred.predict([[0, 1, 2]]))
"""#Classification using Logistic Regression"""
lr = LogisticRegression()
lr.fit(X_train, y_train)
pred = lr.predict(X_val)
print("Accuracy: ", accuracy_score(y_val, pred))
print(classification_report(y_val, pred))
print(confusion_matrix(y_val, pred))
"""#Using TF-IDF Vectorization"""
tfidf_vectorizer = TfidfVectorizer()
tfidf_vectorizer.fit(tweets_list)
X = tfidf_vectorizer.transform(tweets_list)
y = tweets_df["label"].values
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.2, random_state=0)
lr = LogisticRegression()
lr.fit(X_train, y_train)
pred = lr.predict(X_val)
print("Accuracy:", accuracy_score(y_val, pred))
print(classification_report(y_val, pred))
print(confusion_matrix(y_val, pred))
"""#Classification using Naives Bayes"""
MNB = MultinomialNB()
MNB.fit(X_train, y_train)
pred = MNB.predict(X_val)
print(accuracy_score(y_val, pred))
print(classification_report(y_val, pred))
print(confusion_matrix(y_val, pred))
"""# TF-IDF Vectorization"""
tfidf_vectorizer = TfidfVectorizer()
tfidf_vectorizer.fit(tweets_list)
X = tfidf_vectorizer.transform(tweets_list)
y = tweets_df["label"].values
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.2, random_state=0)
MNB = MultinomialNB()
MNB.fit(X_train, y_train)
pred = MNB.predict(X_val)
print("Accuracy: ", accuracy_score(y_val, pred))
print(classification_report(y_val, pred))
print(confusion_matrix(y_val, pred))
#import numpy as np
from flask import Flask, request, jsonify, render_template
app = Flask(__name__)
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict',methods=['GET', 'POST'])
def predict():
'''
For rendering results on HTML GUI
'''
#int_features = 'Safaricom is good'
#final_features = [{'Tweet': int_features}]
#dfPrediction = pd.DataFrame(final_features)
#prediction = svm.predict(dfPrediction['Tweet'])
#output = round(prediction[0], 2)
# yg
#return render_template('index.html', prediction_text='The tweet is {}'.format(output))
if request.method == "POST":
# getting input with name = lname in HTML form
tweetPredict = request.form.get("tweet")
prediction = svm.predict([str[np.array(tweetPredict)]])
output = round(prediction[0], 2)
#return "The tweet is " + tweetPredict
return render_template("index.html", prediction_text='The tweet is {}'.format(output))
@app.route('/predict_api',methods=['POST'])
def predict_api():
'''
For direct API calls trought request
'''
data = request.get_json(force=True)
prediction = svm.predict([np.array(list(data.values()))])
output = prediction[0]
return jsonify(output)
if __name__ == "__main__":
app.run(debug=True)
|