[clean]: removed chunks

sentiment.py

@@ -1,51 +1,28 @@
 import numpy as np # For linear algebra
 import pandas as pd # Data processing, CSV file I/O (e.g. pd.read_csv)
 import matplotlib.pyplot as plt  # For Visualisation
-# get_ipython().run_line_magic('matplotlib', 'inline')
 import seaborn as sns  # For Visualisation
 from bs4 import BeautifulSoup  # For Text Parsing
 
 
 # # IMPORTING DATASET
 
-# In[2]:
-
-
 data = pd.read_csv('Reviews.csv')
 # data
 
 
 # # DATA PREPROCESSING & VISUALISATION
 
-# In[3]:
-
-
 #data.isnull().sum()
 
-
-# In[4]:
-
-
 data=data.dropna()
 #data.isnull().sum()
 
-
-# In[5]:
-
-
 #data.shape
 
-
-# In[6]:
-
-
 score_unique = data['Score'].unique()
 #print(score_unique)
 
-
-# In[7]:
-
-
 #   0-> NEGATIVE REVIEW
 #   1-> NEUTRAL REVIEW
 #   2-> POSTIVE REVIEW
@@ -58,10 +35,6 @@ for i in data['Score']:
     if i>3:
         a.append(2)
 
-
-# In[8]:
-
-
 r_0, r_1, r_2 = 0, 0, 0
 for i in a:
     if i == 0:
@@ -75,10 +48,6 @@ for i in a:
 # print('Neutral Reviews:',r_1)
 # print('Positive Reviews:',r_2)
 
-
-# In[9]:
-
-
 # sns.countplot(a)
 # plt.xlabel('Reviews', color = 'red')
 # plt.ylabel('Count', color = 'red')
@@ -86,42 +55,22 @@ for i in a:
 # plt.title('COUNT PLOT', color = 'r')
 # plt.show()
 
-
-# In[10]:
-
-
 data['sentiment']=a
 #data
 final_dataset = data[['Text','sentiment']]
 #final_dataset
 
-
-# In[11]:
-
-
 data_p=final_dataset[data['sentiment']==2]
 data_n=final_dataset[data['sentiment']==0]
 #len(data_p), len(data_n)
 
-
-# In[12]:
-
-
 datap = data_p.iloc[np.random.randint(1,443766,5000), :]
 datan = data_n.iloc[np.random.randint(1, 82007,5000), :]
 #len(datan), len(datap)
 
-
-# In[13]:
-
-
 data = pd.concat([datap,datan])
 len(data)
 
-
-# In[14]:
-
-
 c=[]
 for i in data['sentiment']:
     if i==0:
@@ -130,17 +79,6 @@ for i in data['sentiment']:
         c.append(1)
 data['sentiment']=c
 
-
-# In[15]:
-
-
-# sns.countplot(data['sentiment'])
-# plt.show()
-
-
-# In[16]:
-
-
 def strip_html(text):
     soup = BeautifulSoup(text, "html.parser")
     return soup.get_text()
@@ -150,12 +88,6 @@ data=data.drop('Text',axis=1)
 
 #data.head()
 
-
-# # MODEL BUILDING
-
-# In[17]:
-
-
 import nltk  #Natural Language Processing Toolkit
 def punc_clean(text):
     import string as st
@@ -164,10 +96,6 @@ def punc_clean(text):
 data['review'] = data['review'].apply(punc_clean)
 #data.head(2)
 
-
-# In[18]:
-
-
 def remove_stopword(text):
     stopword=nltk.corpus.stopwords.words('english')
     stopword.remove('not')
@@ -175,10 +103,6 @@ def remove_stopword(text):
     return ' '.join(a)
 #data['review'] = data['review'].apply(remove_stopword)
 
-
-# In[19]:
-
-
 from sklearn.feature_extraction.text import TfidfVectorizer
 
 vectr = TfidfVectorizer(ngram_range=(1,2),min_df=1)
@@ -187,9 +111,6 @@ vectr.fit(data['review'])
 vect_X = vectr.transform(data['review'])
 
 
-# In[20]:
-
-
 from sklearn.linear_model import LogisticRegression
 
 model = LogisticRegression()
@@ -200,28 +121,11 @@ clf=model.fit(vect_X,data['sentiment'])
 
 # # PREDICTION
 
-# In[21]:
-
-
-clf.predict(vectr.transform(['''Nice look and build quality with moderately fast everything such as refresh rate, display quality, sound, processing, gaming experience and many more ..
-I didn't find any lagging or heating issue..And battery health I won't say great but I'll take that
-
-Only cons I can say about it is camera.. sharpening picture a little much at day light and low light photo you have to compromise.''']))
-
-
-# In[22]:
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-clf.predict(vectr.transform(['''Phone has bugs , and screen quality is poor , Avoid realme. Gaming was just over hyped''']))
-
-
-# In[23]:
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-clf.predict(vectr.transform(['''No lags found super speed and very good performance nice phone in this budget''']))
+# clf.predict(vectr.transform(['''Nice look and build quality with moderately fast everything such as refresh rate, display quality, sound, processing, gaming experience and many more .. I didn't find any lagging or heating issue..And battery health I won't say great but I'll take that, Only cons I can say about it is camera.. sharpening picture a little much at day light and low light photo you have to compromise.''']))
 
+# clf.predict(vectr.transform(['''Phone has bugs , and screen quality is poor , Avoid realme. Gaming was just over hyped''']))
 
-# In[ ]:
+# clf.predict(vectr.transform(['''No lags found super speed and very good performance nice phone in this budget''']))