# Movie Reviews Sentiment Analysis -Binary Classification with Machine Learning

In this Machine Learning Project, we’ll build binary classification that puts movie reviews texts into one of two categories — negative or positive sentiment. We’re going to have a brief look at the Bayes theorem and relax its requirements using the Naive assumption.

Let’s start by importing the Libraries

```import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re # for regex
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import SnowballStemmer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB
from sklearn.metrics import accuracy_score
import pickle```

```data = pd.read_csv('IMDB Dataset.csv')
print(data.shape)
`data.info()`
```#Output
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50000 entries, 0 to 49999
Data columns (total 2 columns):
#   Column     Non-Null Count  Dtype
---  ------     --------------  -----
0   review     50000 non-null  object
1   sentiment  50000 non-null  object
dtypes: object(2)
memory usage: 781.4+ KB```

No null values, Label encode sentiment to 1(positive) and 0(negative)

`data.sentiment.value_counts()`
```#Output
positive    25000
negative    25000
Name: sentiment, dtype: int64```
```data.sentiment.replace('positive',1,inplace=True)
data.sentiment.replace('negative',0,inplace=True)
`data.review[0]`
```#Output
One of the other reviewers has mentioned that after watching just 1 Oz episode you'll be hooked. They are right, as this is exactly what happened with me.<br /><br />The first thing that struck me about Oz was its brutality and unflinching scenes of violence, which set in right from the word GO. Trust me, this is not a show for the faint hearted or timid. This show pulls no punches with regards to drugs, sex or violence. Its is hardcore, in the classic use of the word.<br /><br />It is called OZ as that is the nickname given to the Oswald Maximum Security State Penitentary. It focuses mainly on Emerald City, an experimental section of the prison where all the cells have glass fronts and face inwards, so privacy is not high on the agenda. Em City is home to many..Aryans, Muslims, gangstas, Latinos, Christians, Italians, Irish and more....so scuffles, death stares, dodgy dealings and shady agreements are never far away.<br /><br />I would say the main appeal of the show is due to the fact that it goes where other shows wouldn't dare. Forget pretty pictures painted for mainstream audiences, forget charm, forget romance...OZ doesn't mess around. The first episode I ever saw struck me as so nasty it was surreal, I couldn't say I was ready for it, but as I watched more, I developed a taste for Oz, and got accustomed to the high levels of graphic violence. Not just violence, but injustice (crooked guards who'll be sold out for a nickel, inmates who'll kill on order and get away with it, well mannered, middle class inmates being turned into prison bitches due to their lack of street skills or prison experience) Watching Oz, you may become comfortable with what is uncomfortable viewing....thats if you can get in touch with your darker side.```

## STEPS TO CLEAN THE REVIEWS :

1. Remove HTML tags
2. Remove special characters
3. Convert everything to lowercase
4. Remove stopwords
5. Stemming

### 1. Remove HTML tags

Regex rule : ‘<.*?>’

```def clean(text):
cleaned = re.compile(r'<.*?>')
return re.sub(cleaned,'',text)

data.review = data.review.apply(clean)
data.review[0]```
```#Output
"One of the other reviewers has mentioned that after watching just 1 Oz episode you'll be hooked. They are right, as this is exactly what happened with me.The first thing that struck me about Oz was its brutality and unflinching scenes of violence, which set in right from the word GO. Trust me, this is not a show for the faint hearted or timid. This show pulls no punches with regards to drugs, sex or violence. Its is hardcore, in the classic use of the word.It is called OZ as that is the nickname given to the Oswald Maximum Security State Penitentary. It focuses mainly on Emerald City, an experimental section of the prison where all the cells have glass fronts and face inwards, so privacy is not high on the agenda. Em City is home to many..Aryans, Muslims, gangstas, Latinos, Christians, Italians, Irish and more....so scuffles, death stares, dodgy dealings and shady agreements are never far away.I would say the main appeal of the show is due to the fact that it goes where other shows wouldn't dare. Forget pretty pictures painted for mainstream audiences, forget charm, forget romance...OZ doesn't mess around. The first episode I ever saw struck me as so nasty it was surreal, I couldn't say I was ready for it, but as I watched more, I developed a taste for Oz, and got accustomed to the high levels of graphic violence. Not just violence, but injustice (crooked guards who'll be sold out for a nickel, inmates who'll kill on order and get away with it, well mannered, middle class inmates being turned into prison bitches due to their lack of street skills or prison experience) Watching Oz, you may become comfortable with what is uncomfortable viewing....thats if you can get in touch with your darker side."```

### 2. Remove special characters

```def is_special(text):
rem = ''
for i in text:
if i.isalnum():
rem = rem + i
else:
rem = rem + ' '
return rem

data.review = data.review.apply(is_special)
data.review[0]```
```#Output
'One of the other reviewers has mentioned that after watching just 1 Oz episode you ll be hooked  They are right  as this is exactly what happened with me The first thing that struck me about Oz was its brutality and unflinching scenes of violence  which set in right from the word GO  Trust me  this is not a show for the faint hearted or timid  This show pulls no punches with regards to drugs  sex or violence  Its is hardcore  in the classic use of the word It is called OZ as that is the nickname given to the Oswald Maximum Security State Penitentary  It focuses mainly on Emerald City  an experimental section of the prison where all the cells have glass fronts and face inwards  so privacy is not high on the agenda  Em City is home to many  Aryans  Muslims  gangstas  Latinos  Christians  Italians  Irish and more    so scuffles  death stares  dodgy dealings and shady agreements are never far away I would say the main appeal of the show is due to the fact that it goes where other shows wouldn t dare  Forget pretty pictures painted for mainstream audiences  forget charm  forget romance   OZ doesn t mess around  The first episode I ever saw struck me as so nasty it was surreal  I couldn t say I was ready for it  but as I watched more  I developed a taste for Oz  and got accustomed to the high levels of graphic violence  Not just violence  but injustice  crooked guards who ll be sold out for a nickel  inmates who ll kill on order and get away with it  well mannered  middle class inmates being turned into prison bitches due to their lack of street skills or prison experience  Watching Oz  you may become comfortable with what is uncomfortable viewing    thats if you can get in touch with your darker side '```

### 3. Convert everything to lowercase

```def to_lower(text):
return text.lower()

data.review = data.review.apply(to_lower)
data.review[0]```
```#Output
'one of the other reviewers has mentioned that after watching just 1 oz episode you ll be hooked  they are right  as this is exactly what happened with me the first thing that struck me about oz was its brutality and unflinching scenes of violence  which set in right from the word go  trust me  this is not a show for the faint hearted or timid  this show pulls no punches with regards to drugs  sex or violence  its is hardcore  in the classic use of the word it is called oz as that is the nickname given to the oswald maximum security state penitentary  it focuses mainly on emerald city  an experimental section of the prison where all the cells have glass fronts and face inwards  so privacy is not high on the agenda  em city is home to many  aryans  muslims  gangstas  latinos  christians  italians  irish and more    so scuffles  death stares  dodgy dealings and shady agreements are never far away i would say the main appeal of the show is due to the fact that it goes where other shows wouldn t dare  forget pretty pictures painted for mainstream audiences  forget charm  forget romance   oz doesn t mess around  the first episode i ever saw struck me as so nasty it was surreal  i couldn t say i was ready for it  but as i watched more  i developed a taste for oz  and got accustomed to the high levels of graphic violence  not just violence  but injustice  crooked guards who ll be sold out for a nickel  inmates who ll kill on order and get away with it  well mannered  middle class inmates being turned into prison bitches due to their lack of street skills or prison experience  watching oz  you may become comfortable with what is uncomfortable viewing    thats if you can get in touch with your darker side '```

### 4. Remove stopwords

```def rem_stopwords(text):
stop_words = set(stopwords.words('english'))
words = word_tokenize(text)
return [w for w in words if w not in stop_words]

data.review = data.review.apply(rem_stopwords)
data.review[0]```

### 5. Stem the words

```def stem_txt(text):
ss = SnowballStemmer('english')
return " ".join([ss.stem(w) for w in text])

data.review = data.review.apply(stem_txt)
data.review[0]```
`data.head()`

### CREATING THE MODEL

#### 1. Creating Bag Of Words (BOW)

```X = np.array(data.iloc[:,0].values)
y = np.array(data.sentiment.values)
cv = CountVectorizer(max_features = 1000)
X = cv.fit_transform(data.review).toarray()
print("X.shape = ",X.shape)
print("y.shape = ",y.shape)```
```#Output
X.shape =  (50000, 1000)
y.shape =  (50000,)```
`print(X)`
```#Output
array([[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 1, 0],
...,
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 1, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0]])```

### 2. Train test split

```trainx,testx,trainy,testy = train_test_split(X,y,test_size=0.2,random_state=9)
print("Train shapes : X = {}, y = {}".format(trainx.shape,trainy.shape))
print("Test shapes : X = {}, y = {}".format(testx.shape,testy.shape))```
```#Output
Train shapes : X = (40000, 1000), y = (40000,)
Test shapes : X = (10000, 1000), y = (10000,)```

### 3. Defining the models and Training them

```gnb,mnb,bnb = GaussianNB(),MultinomialNB(alpha=1.0,fit_prior=True),BernoulliNB(alpha=1.0,fit_prior=True)
gnb.fit(trainx,trainy)
mnb.fit(trainx,trainy)
bnb.fit(trainx,trainy)```
```#Output
BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)```

### 4. Prediction and accuracy metrics to choose best model

```ypg = gnb.predict(testx)
ypm = mnb.predict(testx)
ypb = bnb.predict(testx)

print("Gaussian = ",accuracy_score(testy,ypg))
print("Multinomial = ",accuracy_score(testy,ypm))
print("Bernoulli = ",accuracy_score(testy,ypb))```
```#Output
Gaussian =  0.7843
Multinomial =  0.831
Bernoulli =  0.8386```
`pickle.dump(bnb,open('model1.pkl','wb'))`
```rev =  """Terrible. Complete trash. Brainless tripe. Insulting to anyone who isn't an 8 year old fan boy. Im actually pretty disgusted that this movie is making the money it is - what does it say about the people who brainlessly hand over the hard earned cash to be 'entertained' in this fashion and then come here to leave a positive 8.8 review?? Oh yes, they are morons. Its the only sensible conclusion to draw. How anyone can rate this movie amongst the pantheon of great titles is beyond me.

So trying to find something constructive to say about this title is hard...I enjoyed Iron Man? Tony Stark is an inspirational character in his own movies but here he is a pale shadow of that...About the only 'hook' this movie had into me was wondering when and if Iron Man would knock Captain America out...Oh how I wished he had :( What were these other characters anyways? Useless, bickering idiots who really couldn't organise happy times in a brewery. The film was a chaotic mish mash of action elements and failed 'set pieces'...

I found the villain to be quite amusing.

And now I give up. This movie is not robbing any more of my time but I felt I ought to contribute to restoring the obvious fake rating and reviews this movie has been getting on IMDb."""
f1 = clean(rev)
f2 = is_special(f1)
f3 = to_lower(f2)
f4 = rem_stopwords(f3)
f5 = stem_txt(f4)

bow,words = [],word_tokenize(f5)
for word in words:
bow.append(words.count(word))
#np.array(bow).reshape(1,3000)
#bow.shape
word_dict = cv.vocabulary_
pickle.dump(word_dict,open('bow.pkl','wb'))```
```inp = []
for i in word_dict:
inp.append(f5.count(i[0]))
y_pred = bnb.predict(np.array(inp).reshape(1,1000))```

[0]

0 mean negative.