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DataUtility.py
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DataUtility.py
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# Import necessary libraries
import pandas as pd
import numpy as np
import re
from bs4 import BeautifulSoup
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from nltk.sentiment.util import mark_negation
from nltk.corpus import sentiwordnet as swn
import inflect
import string
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
import spacy
import torch
import tensorflow as tf
import gensim
from gensim.models import Word2Vec
from tensorflow.keras.layers import Embedding
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
string.punctuation
# Set up NLTK downloads
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('sentiwordnet')
#initialize variables
stop_words = set(stopwords.words('english'))
stemmer = PorterStemmer()
wordnet_lemmatizer = WordNetLemmatizer()
p = inflect.engine()
tfidf_vectorizer = TfidfVectorizer()
count_vectorizer = CountVectorizer()
# Function to remove punctuation
def remove_punctuation(text):
punctuationfree="".join([i for i in text if i not in string.punctuation])
return punctuationfree
# Function to remove original text from Google Translated data
def remove_original(text):
translated_text = re.search(r'\(Translated by Google\)(.*?)\(Original\)', text, re.DOTALL)
if translated_text:
translated_text = translated_text.group(1).strip()
return translated_text if translated_text else text
# Function to remove emojis from text
def remove_emojis(text):
emoji_pattern = re.compile("["
u"\U0001F600-\U0001F64F"
u"\U0001F300-\U0001F5FF"
u"\U0001F680-\U0001F6FF"
u"\U0001F700-\U0001F77F"
u"\U0001F780-\U0001F7FF"
u"\U0001F800-\U0001F8FF"
u"\U0001F900-\U0001F9FF"
u"\U0001FA00-\U0001FA6F"
u"\U0001FA70-\U0001FAFF"
u"\U0001FB00-\U0001FBFF"
u"\U0001F004-\U0001F0CF"
u"\U0001F10D-\U0001F251"
u"\U0001F004-\U0001F251"
"]+", flags=re.UNICODE)
return emoji_pattern.sub(r'', text)
# Function to remove URLs, HTML tags, and special characters
def remove_url_html_spc(text):
text = re.sub(r'http\S+|www\S+|https\S+', '', text)
text = BeautifulSoup(text, 'html.parser').get_text()
text = re.sub(r'[^\w\s]', '', text)
text = ' '.join(text.split())
text = text.strip()
return text
# Function to remove stopwords
def remove_stopwords(text):
filtered_tokens = [token for token in text if token not in stop_words]
return filtered_tokens
# Function for stemming
def handle_stemming(text):
stemmed_tokens = [stemmer.stem(token) for token in text]
return stemmed_tokens
# Function for lemmatization
def lemmatizer(text):
lemm_text = [wordnet_lemmatizer.lemmatize(word) for word in text]
return lemm_text
# Function to handle negation
def handle_negate_text(text):
negated_tokens = mark_negation(text)
return negated_tokens
# Function to replace numbers with words
def replace_numbers_with_words(text):
words = text.split()
for i, word in enumerate(words):
if word.isnumeric():
words[i] = p.number_to_words(word)
return ' '.join(words)
# Function to calculate sentiment
def calculate_sentiment(tokens):
sentiment_score = 0
sentiment = ""
for token in tokens:
senti_synsets = list(swn.senti_synsets(token))
if senti_synsets:
# Use the average sentiment score for each sense of the word
token_sentiment = sum([s.pos_score() - s.neg_score() for s in senti_synsets]) / len(senti_synsets)
sentiment_score += token_sentiment
if sentiment_score > 0:
sentiment = "Positive"
elif sentiment_score == 0:
sentiment = "Neutral"
else:
sentiment = "Negative"
return sentiment
# Class for data-related utilities
class dataUtils():
def __init__(self):
pass
def GetGoogleReviewData(self):
path = 'Dataset/review-Michigan_10.json'
google_review_data = pd.read_json(path, lines=True)
return google_review_data
# Class for data preprocessing
class preProcessing():
def __init__(self):
pass
def removeEmptyReview(self,data):
data = data.dropna(subset=['text'])
return data
def HandleTranslatedData(self,data):
data["processed_review"] = data['text'].apply(lambda x:remove_original(x))
def handlePunctuation(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x: remove_punctuation(x))
def DoLowerCase(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x: x.lower())
def removeEmoji(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x: remove_emojis(x))
def handleURLAndHTMLAndSpecialCharacter(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x:remove_url_html_spc(x))
def handleStopWords(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x: remove_stopwords(x))
#just wrote this, but I prefer doing Lemmatization over stemming
def handleStemming(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x: handle_stemming(x))
def handleLemmatizer(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x: lemmatizer(x))
def handleNegation(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x: handle_negate_text(x))
def handleNumericValue(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x: replace_numbers_with_words(x))
def tokenizeText(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x: word_tokenize(x))
#rule-based sentiment analysis
def addSentiwordAnalysis(self,data):
data['sentiment'] = data['processed_review'].apply(lambda x: calculate_sentiment(x))
def joinTextData(self,data):
data['processed_review'] = data['processed_review'].apply(lambda x: ' '.join(x))
# Class for text vectorization
class textVectorizer():
def __init__(self):
pass
#for ML models we can use TFIDF , bagOfWords, spacy word embeddings for vectorization
def handleTFIDFVectorizer(self,data):
tfidf_matrix = tfidf_vectorizer.fit_transform(data['processed_review'])
return tfidf_matrix
def bagOfWords(self,data):
count_matrix = count_vectorizer.fit_transform(data['processed_review'])
return count_matrix
def spacyWordEmbeddings(self,data):
nlp = spacy.load("en_core_web_sm")
Spacy_Embeddings = data['processed_review'].apply(lambda x: nlp(x).vector)
return Spacy_Embeddings
#for DL model we can use PyTorch, Keras -> word2vec
def pytorchWord2VecEmbedding(self,data):
sentences = data['processed_review'].tolist()
word2vec_model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, sg=0)
word2vec_matrix = torch.Tensor([word2vec_model.wv[word] for sentence in sentences for word in sentence])
return word2vec_matrix
def kerasWord2VecEmbedding(self,data):
tokenizer = Tokenizer()
tokenizer.fit_on_texts(data['processed_review'])
sequences = tokenizer.texts_to_sequences(data['processed_review'])
padded_sequences = pad_sequences(sequences)
return padded_sequences