add code for wordcloud generator

scripts/wordcloudGenerator/requirements.txt

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+click==8.1.3
+contourpy==1.0.5
+cycler==0.11.0
+fonttools==4.37.4
+joblib==1.2.0
+kiwisolver==1.4.4
+matplotlib==3.6.0
+nltk==3.7
+numpy==1.23.3
+packaging==21.3
+pandas==1.5.0
+Pillow==9.2.0
+pyparsing==3.0.9
+python-dateutil==2.8.2
+pytz==2022.4
+regex==2022.9.13
+six==1.16.0
+textblob==0.17.1
+tqdm==4.64.1
+wordcloud==1.8.2.2

scripts/wordcloudGenerator/wordcloudGenerator.py

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