mirror of
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204 lines
6.1 KiB
Python
204 lines
6.1 KiB
Python
import pandas as pd
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import random
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from nltk.corpus import stopwords
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from nltk.tokenize import RegexpTokenizer
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import nltk
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import string
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from textblob import TextBlob
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import wordcloud
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from matplotlib import pyplot as plt
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from nltk.stem import WordNetLemmatizer
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from nltk.stem import PorterStemmer
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from textblob.sentiments import NaiveBayesAnalyzer
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from nltk import FreqDist
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# list to decide colours of positive & negative words
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pos_word_list=[]
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neg_word_list=[]
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class SimpleGroupedColorFunc(object):
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def __init__(self, color_to_words, default_color):
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self.word_to_color = {word: color
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for (color, words) in color_to_words.items()
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for word in words}
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self.default_color = default_color
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def __call__(self, word, **kwargs):
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return self.word_to_color.get(word, self.default_color)
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class GroupedColorFunc(object):
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def __init__(self, color_to_words, default_color):
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self.color_func_to_words = [
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(wordcloud.get_single_color_func(color), set(words))
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for (color, words) in color_to_words.items()]
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self.default_color_func = wordcloud.get_single_color_func(default_color)
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def get_color_func(self, word):
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try:
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color_func = next(
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color_func for (color_func, words) in self.color_func_to_words
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if word in words)
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except StopIteration:
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color_func = self.default_color_func
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return color_func
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def __call__(self, word, **kwargs):
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return self.get_color_func(word)(word, **kwargs)
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# # function to convert a csv to string format
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# def csv2string(file, negative, positive):
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# s1 = "no negative"
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# s2 = "no positive"
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# dataset = pd.read_csv(file)
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# neg = dataset[negative].head(10000)
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# pos = dataset[positive].head(10000)
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# neg_list = neg.tolist()
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# random.shuffle(neg_list)
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# pos_list = pos.tolist()
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# random.shuffle(pos_list)
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# final = neg_list + pos_list
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# random.shuffle(final)
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# review = ' '.join(final).lower()
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# review = review.replace(s2,"")
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# review = review.replace(s1,"")
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# print('review string has been generated... Calling Wordcloud Generator')
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# wordcloud_generator(review)
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# function to convert a csv to string format
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def csv2string(file, header):
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s1 = "no negative"
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s2 = "no positive"
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dataset = pd.read_csv(file)
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rev = dataset[header].head(10000)
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rev_list = rev.tolist()
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random.shuffle(rev_list)
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review = ' '.join(rev_list).lower()
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review = review.replace(s2,"")
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review = review.replace(s1,"")
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print('review string has been generated... Calling Wordcloud Generator')
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wordcloud_generator(review)
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# function to convert a text file to string format
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def txt2string(file):
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s1 = "no negative\n"
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s2 = "no positive\n"
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with open(file) as f:
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review = f.read().lower()
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review = review.replace(s2,"")
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review = review.replace(s1,"")
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print('review string has been generated... Calling Wordcloud Generator')
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wordcloud_generator(review)
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# function to determine the polarity of a given word
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def word_polarity(tokens):
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counter=0
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for word in tokens:
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testimonial = TextBlob(word, analyzer=NaiveBayesAnalyzer())
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p = testimonial.sentiment.p_pos
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n = testimonial.sentiment.p_neg
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print(p)
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print(n)
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print(counter)
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counter+=1
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print(word)
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print("~~~~~~~~~")
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if p>0.5:
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pos_word_list.append(word)
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elif n>0.5:
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neg_word_list.append(word)
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# function that creates the wordcloud based on frequency of words
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def calc_freq(tokens, color_function):
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frequency = {}
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for item in tokens:
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frequency[item] = tokens.count(item)
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cloud = wordcloud.WordCloud(color_func=color_function,width=800, height=400)
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cloud.generate_from_frequencies(frequency)
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cloud.to_file("/Users/dakshjain/Desktop/wc.png")
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print("File saved in local system...")
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return cloud.to_array()
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def wordcloud_generator(text):
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nltk.download('stopwords')
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nltk.download('wordnet')
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nltk.download('averaged_perceptron_tagger')
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nltk.download('movie_reviews')
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nltk.download('punkt')
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nltk.download('omw-1.4')
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tokenizer = RegexpTokenizer(r'\w+')
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tokens = tokenizer.tokenize(text)
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print("tokens created...")
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stop_words = stopwords.words('english')
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filtered_token = []
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for w in tokens:
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if w not in stop_words and len(w)>3:
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filtered_token.append(w)
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print("stop words removed...")
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lemmatizer = WordNetLemmatizer()
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lemmatized_filtered_token = []
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for w in filtered_token:
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if len(w)>3:
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lemmatized_filtered_token.append(lemmatizer.lemmatize(w))
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pos_tagged_token = nltk.pos_tag(lemmatized_filtered_token)
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adjective_tokens_0 = []
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for w in pos_tagged_token:
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if w[1] == 'JJ' and len(w[0])>3:
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adjective_tokens_0.append(w[0])
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print("Level 1 Adjective sorting done...")
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x = nltk.pos_tag(adjective_tokens_0)
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adjective_tokens_1 = []
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for w in x:
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if w[1] == 'JJ' and len(w[0])>3:
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adjective_tokens_1.append(w[0])
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print("Level 2 Adjective sorting done...")
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y = nltk.pos_tag(adjective_tokens_1)
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adjective_tokens_2 = []
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for w in y:
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if w[1] == 'JJ' and len(w[0])>3:
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adjective_tokens_2.append(w[0])
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print("Level 3 Adjective sorting done...")
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freq_dist = FreqDist(adjective_tokens_2)
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common_words = freq_dist.most_common(5)
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max_freq_list = []
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for w in common_words:
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max_freq_list.append(w[0])
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print("50 most common words selected for colour sorting... Polarity Finding function called...")
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word_polarity(max_freq_list)
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color_to_words = {
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'#00ff00': pos_word_list,
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'red': neg_word_list
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}
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default_color = 'grey'
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print("Colours associated with given words...")
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grouped_color_func = GroupedColorFunc(color_to_words, default_color)
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print("Calling Wordcloud Creator...")
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myimage = calc_freq(adjective_tokens_2,grouped_color_func)
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print("DISPLAYING THE WORDCLOUD !!")
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plt.figure( figsize=(20,10), facecolor='k')
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plt.imshow(myimage)
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plt.axis('off')
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plt.show()
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# depending upon your input data call any of the 2 functions.
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# For example ---
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csv2string('tripadvisor_hotel_reviews.csv', 'Review')
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# txt2string('file.txt')
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