process:

  Load the data set.

  Do pre-pocessing like remove punctuation, stopwords, etc..

  Convert text data into vector format.

  Define the independent and dependent variable..

  Build the deep neural networks.

  Initiate activation and optimizer functions according to the problem..

  Compile and fit the DNN model.

  Split the data into train and testing set.

  Fit the training set into the model.

  Predict the test results using DNN.

  Calculate the accuracy, precision and recall.

#import necessary libraries

import warnings

warnings.filterwarnings(“ignore”)

import pandas as pd

import numpy as np

import time

import re

from sklearn.feature_extraction.text import TfidfVectorizer

import matplotlib.pyplot as plt

import seaborn as sns

from keras.models import Sequential

from keras.layers import Dense

from sklearn.model_selection import train_test_split

from keras.utils import np_utils

from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

#column header

names = [‘comments’,’type’]

#load data

data = pd.read_table(‘…/practice/amazon_cells_labelled.txt’,sep=”\t”,names = names)

#check missing values

print(“Checking missing values\n\n”)

print(data.isnull().sum())

#make it as a data frame

df = pd.DataFrame(data)

#counts in each class

print(“\n”)

print(“Counts in each class\n”)

print(df[‘type’].value_counts())

#Features

X = df[‘comments’]

y = df[‘type’]

#change text lower cases and removal of white spaces

lower_text = []

for i in range(0,len(X)):

s = str(X[i])

s1 = s.strip()

lower_text.append(s1.lower())

#print(“After converting text to lower case\n\n”,lower_text)

#Remove punctuation

punc_text = []

for i in range(0,len(lower_text)):

s2 = (lower_text[i])

s3 = re.sub(r'[^\w\s2]’,”,s2)

punc_text.append(s3)

#print(“After removed punctuation\n\n”,punc_text)

#Word vectorization

#Initialize the TF-IDF vectorizer

tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5,max_df = 0.7,norm=’l2′, encoding=’latin-1′, ngram_range=(1, 2),

stop_words=’english’)

#transform independent variable using TF-IDF vectorizer

print(“\n”)

X_tfidf = tfidf.fit_transform(punc_text)

print(“After vectorized text data\n\n”,X_tfidf)

#Split the data into train and testing

X_train, X_test, Y_train, Y_test = train_test_split(X_tfidf, y, test_size=0.1, random_state=0)

#Print training data

print(“\n”)

print(“Training data\n\n”,X_train,”\n”,Y_train)

print(“\n\n”)

#Print testing data

print(“Testing data\n\n”,X_test)

print(“\n\n”)

#make dependent variable categorical

Y_train = np_utils.to_categorical(Y_train,num_classes=2)

Y_test = np_utils.to_categorical(Y_test,num_classes=2)

batch_size = 20

#shape of input

n_cols_2 = X_train.shape[1]

#create deep neural networks

model_2 = Sequential()

#add layers to model

model_2.add(Dense(250, activation=’relu’, input_shape=(n_cols_2,)))

model_2.add(Dense(250, activation=’relu’))

model_2.add(Dense(250, activation=’relu’))

model_2.add(Dense(2, activation=’softmax’))

print(model_2.summary())

#Compile the model

model_2.compile(optimizer=’adam’, loss=’binary_crossentropy’, metrics=[‘accuracy’])

#Here we train the Network.

start_time = time.time()

model_2.fit(X_train, Y_train, batch_size = batch_size, epochs = 10, verbose = 2)

end_time = time.time()

elapsed_time = end_time – start_time

print(“Time to train model: %.3f seconds” % elapsed_time)

#Evaluate the network

start_time = time.time()

score,acc = model_2.evaluate(X_test,Y_test,verbose = 2,batch_size = batch_size)

end_time = time.time()

elapsed_time = end_time – start_time

print(“Time to evaluate model: %.3f seconds” % elapsed_time)

print(“\n”)

#Predict the test results

prediction = model_2.predict(X_test)

length = len(prediction)

y_label = np.argmax(Y_test,axis=1)

predict_label = np.argmax(prediction,axis=1)

#classification report

print(“Confusion Matrix\n”,confusion_matrix(y_label,predict_label))

print(“\n”)

print(“Classification Report\n”,classification_report(y_label,predict_label))

print(“\n”)

print(“Accuracy : “,accuracy_score(y_label,predict_label)*100)