#Loading the data to the variable as DataFrame #importing pandas library import pandas as pd #import counter vectorized function from sklearn from sklearn.feature_extraction.text import CountVectorizer count=CountVectorizer() data=pd.read_csv("Train.csv") data.head()

#finding the positive and negative text in Data pos=data[data['label']==1] neg=data[data['label']==0] print("Positive text \n",pos.head()) print("\nNegative text \n",neg.head())

#Plotting the Postive vs Negative in piechart. #Importing matplotlib library to plot pie chart. import matplotlib.pyplot as plt fig=plt.figure(figsize=(5,5)) temp=[pos['label'].count(),neg['label'].count()] plt.pie(temp,labels=["Positive","Negative"],autopct ='%2.1f%%',shadow = True,startangle = 50,explode=(0, 0.3)) plt.title('Positive vs Negative')

#importing re library import re #Defining preprocessing function to process the data def preprocess(text): text=re.sub('<[^>]*>','',text) emoji=re.findall('(?::|;|=)(?:-)?(?:\)|\(|D|P)',text) text=re.sub('[\W]+',' ',text.lower()) +' '.join(emoji).replace('-','') return text

#Applying the function preprocess on the data data['text']=data['text'].apply(preprocess)

#Displaying the dataframe after applying the preprocessing. data.head()

#Defining a function called tokenizer which splits the sentence def tokenizer(text): return text.split() tokenizer("He was joyful as he was working in good environment")

#Importing stemmer function from NLTK library from nltk.stem.porter import PorterStemmer porter=PorterStemmer() #Defining function for Tokenizer porter def tokenizer_porter(text): return [porter.stem(word) for word in text.split()]

#Importing NLTK library. import nltk nltk.download('stopwords') from nltk.corpus import stopwords stop=stopwords.words('english') #Importing Word cloud from wordcloud import WordCloud

#getting positive and negative data positive_data = data[ data['label'] == 1] positive_data = positive_data['text'] negative_data = data[data['label'] == 0] negative_data= negative_data['text']

#Defining the function to plot the data in wordcloud def plot_wordcloud(data, color = 'white'): words = ' '.join(data) clean_word = " ".join([word for word in words.split() if(word!='movie' and word!='film')]) wordcloud = WordCloud(stopwords=stop,background_color=color,width=2500,height=2000).generate(clean_word) plt.figure(1,figsize=(10, 7)) plt.imshow(wordcloud) plt.axis('off') plt.show()

#Printing the positive data in wordcloud print("Positive words") plot_wordcloud(positive_data,'white')

#Printing the negative data in wordcloud print("Negative words") plot_wordcloud(negative_data,'black')

#importing tfiVectorizer from sklearn for feature extraction. from sklearn.feature_extraction.text import TfidfVectorizer tfid=TfidfVectorizer(strip_accents=None,preprocessor=None,lowercase=False,use_idf=True,norm='l2',tokenizer=tokenizer_porter,smooth_idf=True) y=data.label.values #scaling the data x=tfid.fit_transform(data.text)

#splitting the train and test split using train_test_split function of sklearn from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test=train_test_split(x,y,random_state=1,test_size=0.5,shuffle=False)

#Importing Logisitic RegressionCV from sklearn library from sklearn.linear_model import LogisticRegressionCV model=LogisticRegressionCV(cv=6,scoring='accuracy',random_state=0,n_jobs=-1,verbose=3,max_iter=500).fit(X_train,y_train) y_pred = model.predict(X_test)

#Importing metrics from sklesrn to calculate accuracy from sklearn import metrics # Accuracy of our built model print("Accuracy of our model:",metrics.accuracy_score(y_test, y_pred))