SMS Spam Detector using NLTK

Building an SMS Spam Detector on Python using NLTK Library

Imports and Configuration

import nltk
import pandas as pd

nltk.download_shell()

Reading the File

Here is the Link to Data

messages = [line.rstrip() for line in open('smsspamcollection/SMSSpamCollection')]

print(len(messages))

5574

messages[0]

'ham\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...'

Exploring the messages

for mess_no,message in enumerate(messages[:10]):
    print(mess_no,message)
    print('\n')

Creating a Dataframe

messages = pd.read_csv('smsspamcollection/SMSSpamCollection',sep='\t',
                      names=['label','message'])

messages.head()

	label	message
0	ham	Go until jurong point, crazy.. Available only ...
1	ham	Ok lar... Joking wif u oni...
2	spam	Free entry in 2 a wkly comp to win FA Cup fina...
3	ham	U dun say so early hor... U c already then say...
4	ham	Nah I don't think he goes to usf, he lives aro...

messages.describe()

	label	message
count	5572	5572
unique	2	5169
top	ham	Sorry, I'll call later
freq	4825	30

messages.groupby('label').describe()

	message
	count	unique	top	freq
label
ham	4825	4516	Sorry, I'll call later	30
spam	747	653	Please call our customer service representativ...	4

messages['length'] = messages['message'].apply(len)

messages.head()

	label	message	length
0	ham	Go until jurong point, crazy.. Available only ...	111
1	ham	Ok lar... Joking wif u oni...	29
2	spam	Free entry in 2 a wkly comp to win FA Cup fina...	155
3	ham	U dun say so early hor... U c already then say...	49
4	ham	Nah I don't think he goes to usf, he lives aro...	61

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

messages['length'].plot.hist(bins=150)

messages['length'].describe()

count    5572.000000
mean       80.489950
std        59.942907
min         2.000000
25%        36.000000
50%        62.000000
75%       122.000000
max       910.000000
Name: length, dtype: float64

Outlier

messages[messages['length']==910]['message'].iloc[0]

"For me the love should start with attraction.i should feel that I need her every time around me.she should be the first thing which comes in my thoughts.I would start the day and end it with her.she should be there every time I dream.love will be then when my every breath has her name.my life should happen around her.my life will be named to her.I would cry for her.will give all my happiness and take all her sorrows.I will be ready to fight with anyone for her.I will be in love when I will be doing the craziest things for her.love will be when I don't have to proove anyone that my girl is the most beautiful lady on the whole planet.I will always be singing praises for her.love will be when I start up making chicken curry and end up makiing sambar.life will be the most beautiful then.will get every morning and thank god for the day because she is with me.I would like to say a lot..will tell later.."

messages.hist(column='length',by='label',bins=60,figsize=(12,4))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002A1B9ADBE48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002A1B9BC8208>],
      dtype=object)

import string

mess = 'Sample message! Notice: it has punctuation'

string.punctuation

'!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~'

#remove punctuation from the mess

nopunc = [c for c in mess if c not in string.punctuation]

from nltk.corpus import stopwords

stopwords.words('English')

nopunc = ''.join(nopunc) # remove spaces from punc removed str

nopunc

'Sample message Notice it has punctuation'

nopunc.split()

['Sample', 'message', 'Notice', 'it', 'has', 'punctuation']

clean_mess = [word for word in nopunc.split() if word.lower() not in stopwords.words('English')]

clean_mess

['Sample', 'message', 'Notice', 'punctuation']

#function

def text_process(mess):
    """
    1- Remove punctuation
    2- Remove stop words
    3- Return list of clean words
    """
    nopunc = [char for char in mess if char not in string.punctuation]
    
    nopunc = ''.join(nopunc)
    
    return [word for word in nopunc.split() if word.lower() not in stopwords.words('English')]

messages['message'].head(5).apply(text_process)

0    [Go, jurong, point, crazy, Available, bugis, n...
1                       [Ok, lar, Joking, wif, u, oni]
2    [Free, entry, 2, wkly, comp, win, FA, Cup, fin...
3        [U, dun, say, early, hor, U, c, already, say]
4    [Nah, dont, think, goes, usf, lives, around, t...
Name: message, dtype: object

from sklearn.feature_extraction.text import CountVectorizer

bow_transformer = CountVectorizer(analyzer=text_process).fit(messages['message'])

print(len(bow_transformer.vocabulary_))

11425

mess4 = messages['message'][3]

print(mess4)

U dun say so early hor... U c already then say...

bow4 = bow_transformer.transform([mess4])

print(bow4)

  (0, 4068)	2
  (0, 4629)	1
  (0, 5261)	1
  (0, 6204)	1
  (0, 6222)	1
  (0, 7186)	1
  (0, 9554)	2

bow_transformer.get_feature_names()[9554]

'say'

messages_bow = bow_transformer.transform(messages['message'])

print('Shape of Sparse Matrix: ',messages_bow.shape)

Shape of Sparse Matrix:  (5572, 11425)

messages_bow.nnz

50548

from sklearn.feature_extraction.text import TfidfTransformer

tfidf_transformer = TfidfTransformer().fit(messages_bow)

tfidf4 = tfidf_transformer.transform(bow4)

print(tfidf4)

  (0, 9554)	0.5385626262927564
  (0, 7186)	0.4389365653379857
  (0, 6222)	0.3187216892949149
  (0, 6204)	0.29953799723697416
  (0, 5261)	0.29729957405868723
  (0, 4629)	0.26619801906087187
  (0, 4068)	0.40832589933384067

tfidf_transformer.idf_[bow_transformer.vocabulary_['university']]

8.527076498901426

messages_tfidf = tfidf_transformer.transform(messages_bow)

from sklearn.naive_bayes import MultinomialNB

spam_detect_model = MultinomialNB().fit(messages_tfidf,messages['label'])

spam_detect_model.predict(tfidf4)[0]

'ham'

messages['label'][3]

'ham'

all_pred = spam_detect_model.predict(messages_tfidf)

from sklearn.model_selection import train_test_split

msg_train, msg_test, label_train, label_test = train_test_split(messages['message'],messages['label'],test_size=0.3)

Building up a Pipeline for Classification

from sklearn.pipeline import Pipeline

pipeline = Pipeline([
    ('bow',CountVectorizer(analyzer=text_process)),
    ('tfidf',TfidfTransformer()),
    ('classifier',MultinomialNB())
])

pipeline.fit(msg_train,label_train)

Pipeline(memory=None,
     steps=[('bow', CountVectorizer(analyzer=<function text_process at 0x000002A1B9DE3950>,
        binary=False, decode_error='strict', dtype=<class 'numpy.int64'>,
        encoding='utf-8', input='content', lowercase=True, max_df=1.0,
        max_features=None, min_df=1, ngram_range=(1, 1), preprocesso...f=False, use_idf=True)), ('classifier', MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))])

Predictions and Evaluation

predictions = pipeline.predict(msg_test)

from sklearn.metrics import classification_report

print(classification_report(label_test,predictions))

              precision    recall  f1-score   support

         ham       0.96      1.00      0.98      1462
        spam       1.00      0.70      0.82       210

   micro avg       0.96      0.96      0.96      1672
   macro avg       0.98      0.85      0.90      1672
weighted avg       0.96      0.96      0.96      1672