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146 lines
4.7 KiB
Python
146 lines
4.7 KiB
Python
## analysing text, to predict whether the rest review is positive or not
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#supervised learning..
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# import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# import streamlit as st
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#importing dataset
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dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\t', quoting = 3)
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#Cleaning the texts .. remove stopwords etc, stemming.. similar words like love or loved
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# makes all in small letters
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# sparse matrix
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#Bag of words basis
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#First cleaning is applied to the first record only
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import re #library used in cleaning
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review = re.sub('[^a-zA-Z]',' ',dataset['Review'][0]) #.. removing letters other than a to z
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review = review.lower() # at this stage Review is availble in the form of a string
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#remove non significant words
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import nltk
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# nltk.download('stopwords')
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from nltk.corpus import stopwords
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review = review.split() # the split finction converts the string into list
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review = [word for word in review if not word in set(stopwords.words('english'))]
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#Stemming .. replace words which are similar like love and loved to the root word like love
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from nltk.stem.porter import PorterStemmer
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ps = PorterStemmer()
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ml = []
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for word in review:
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st = ps.stem(word)
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ml.append(st)
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review = ml
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#joining the words to make a string of cleaned words
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review = ' '.join(review) # list is converted back to string
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corpus = []
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corpus.append(review) # string is put back into a list (as a combined string)
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# print(len(corpus))
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##---------------------------------
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#Now the above cleaning is applied to all the remaning records from index 1 to 999
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for i in range(1,1000):
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review1 = re.sub('[^a-zA-Z]',' ',dataset['Review'][i])
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review1 = review1.lower()
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review1 = review1.split()
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review1 = [word for word in review1 if not word in set(stopwords.words('english'))]
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ml1 = []
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for word in review1:
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st1 = ps.stem(word)
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ml1.append(st1)
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review1 = ml1
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review1 = ' '.join(review1)
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corpus.append(review1)
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##-----------------------------------
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# To create a bag of words model
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# it is same as creating a sparse matrix through the process of tokenisation
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# ie to create a separate column for each of the word
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# so finally what we get is a review, column for each word and its frequency
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# The sparse matrix would essentially contain all the required features / feature matrix
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# Once we get the above bag of words, we shud be apply the classification template
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# this process is handled through a class called as countvectorizer
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from sklearn.feature_extraction.text import CountVectorizer
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cv = CountVectorizer(max_features = 1500)
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X10 = cv.fit_transform(corpus)
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# print(cv.get_feature_names())
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X1 = X10.toarray()
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y = dataset.iloc[:, 1].values
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##-----------------------------------
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# Splitting the dataset into the Training set and Test set
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X1, y, test_size = 0.20, random_state = 0)
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# Fitting Naive Bayes to the Training set
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from sklearn.naive_bayes import GaussianNB
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classifier = GaussianNB()
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classifier.fit(X_train, y_train)
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# plt.hist(X_train)
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plt.hist(y_train, label='positive (1) and negative (0)')
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plt.legend()
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plt.show()
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# Predicting the Test set results
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y_pred = classifier.predict(X_test)
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# Making the Confusion Matrix
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from sklearn.metrics import confusion_matrix, accuracy_score
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cm = confusion_matrix(y_test, y_pred)
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print('Confusion Matrix: \n', cm)
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acc_cm =(cm[0,0]+cm[1,1])/(cm[0,0]+cm[0,1]+cm[1,0]+cm[1,1])
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print('Accuracy of the model based on confusion matrix: ', acc_cm)
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acc_score1 = accuracy_score(y_test, y_pred)
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print('Accuracy score of the model: ', acc_score1)
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#- ------------------------------------------------------------
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#classifying the sehensence entered by the user based on the above model
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userinput = input("Enter your input : ")
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# userinput = st.text_area("Enter your input : ")
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review2 = re.sub('[^a-zA-Z]',' ',userinput) #.. removing letters other than a to z
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review2 = review2.lower()
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review2 = review2.split()
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review2 = [word for word in review2 if not word in set(stopwords.words('english'))]
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ml13 = []
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for word in review2:
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st13 = ps.stem(word)
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ml13.append(st13)
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review2 = ml13
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#joining the words to make a string of cleaned words
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review2 = ' '.join(review2)
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import copy
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corpus_copy = copy.deepcopy(corpus)
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corpus_copy.append(review2)
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#It is necessary to put the new record below the existing records so that the new record is also
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#arranged in the same manner..
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cv = CountVectorizer(max_features = 1500)
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X111 = cv.fit_transform(corpus_copy).toarray()
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y_pred1 = classifier.predict(X111[-1:][:])
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print(y_pred1)
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if y_pred1 == 1:
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print("The customer feedback is positive")
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else:
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print("The customer feedback is negative")
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corpus_copy = []
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