Update README.md

README.md

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+pip install vaderSentiment
+
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+analyser = SentimentIntensityAnalyzer()
+
+analyser.polarity_scores("I hate watching movies")  
+
+import nltk
+from nltk.tokenize import word_tokenize, RegexpTokenizer
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+nltk.download('all')
+
+import numpy as np
+
+sentence = """I love dancing & painting"""
+tokenized_sentence = nltk.word_tokenize(sentence)
+
+from nltk import word_tokenize
+from typing import List
+
+Analyzer = SentimentIntensityAnalyzer()
+
+pos_word_list=[]
+neu_word_list=[]
+neg_word_list=[]
+pos_score_list=[]
+neg_score_list=[]
+score_list=[]
+for word in tokenized_sentence:
+    if (Analyzer.polarity_scores(word)['compound']) >= 0.1:
+        pos_word_list.append(word)
+        score_list.append(Analyzer.polarity_scores(word)['compound'])
+    elif (Analyzer.polarity_scores(word)['compound']) <= -0.1:
+        neg_word_list.append(word)
+        score_list.append(Analyzer.polarity_scores(word)['compound'])
+    else:
+        neu_word_list.append(word)
+        score_list.append(Analyzer.polarity_scores(word)['compound'])
+
+print('Positive:',pos_word_list)
+print('Neutral:',neu_word_list)
+print('Negative:',neg_word_list) 
+print('Score:', score_list)
+score = Analyzer.polarity_scores(sentence)
+print('\nScores:', score)
+
+predict_log=score.values()
+value_iterator=iter(predict_log)
+neg_prediction=next(value_iterator)
+neu_prediction=next(value_iterator)
+pos_prediction=next(value_iterator)
+
+prediction_list=[neg_prediction, pos_prediction]
+prediction_list_array=np.array(prediction_list)
+
+
+def predict():
+        probs = []
+        for text in texts:
+            offset = (self.score(text) + 1) / 2.
+            binned = np.digitize(5 * offset, self.classes) + 1
+            simulated_probs = scipy.stats.norm.pdf(self.classes, binned, scale=0.5)
+            probs.append(simulated_probs)
+        return np.array(probs)
+
+latex_special_token = ["!@#$%^&*()"]
+
+import operator
+
+def generate(text_list, attention_list, latex_file, color_neg='red', color_pos='green', rescale_value = False):
+  print("hello")
+  attention_list = rescale(attention_list)
+  word_num = len(text_list)
+  print(len(attention_list))
+  print(len(text_list))
+ 
+    
+  text_list = clean_word(text_list)
+  with open(latex_file,'w') as f:
+    f.write(r'''\documentclass[varwidth]{standalone}
+\special{papersize=210mm,297mm}
+\usepackage{color}
+\usepackage{tcolorbox}
+\usepackage{CJK}
+\usepackage{adjustbox}
+\tcbset{width=0.9\textwidth,boxrule=0pt,colback=red,arc=0pt,auto outer arc,left=0pt,right=0pt,boxsep=5pt}
+\begin{document}
+\begin{CJK*}{UTF8}{gbsn}'''+'\n')
+    string = r'''{\setlength{\fboxsep}{0pt}\colorbox{white!0}{\parbox{0.9\textwidth}{'''+"\n"
+    for idx in range(len(attention_list)):
+      if attention_list[idx] > 0:
+        string += "\\colorbox{%s!%s}{"%(color_pos, attention_list[idx])+"\\strut " + text_list[idx]+"} "
+      else:
+        string += "\\colorbox{%s!%s}{"%(color_neg, -attention_list[idx])+"\\strut " + text_list[idx]+"} "
+          
+    string += "\n}}}"
+    f.write(string+'\n')
+    f.write(r'''\end{CJK*}
+\end{document}''')
+
+  
+
+def rescale(input_list):
+  
+  the_array = np.asarray(input_list)
+  the_max = np.max(abs(the_array))
+  rescale = the_array/the_max
+  rescale = rescale*100
+  rescale = np.round(rescale, 3)
+  
+  '''
+  the_array = np.asarray(input_list)
+  the_max = np.max(the_array)
+  the_min = np.min(the_array)
+  rescale = ((the_array - the_min)/(the_max-the_min))*100
+  for i in rescale:
+    print(rescale)
+  '''
+  
+  return rescale.tolist()
+
+def clean_word(word_list):
+	new_word_list = []
+	for word in word_list:  
+		for latex_sensitive in ["\\", "%", "&", "^", "#", "_",  "{", "}"]:
+			if latex_sensitive in word:
+				word = word.replace(latex_sensitive, '\\'+latex_sensitive)
+		new_word_list.append(word)
+	return new_word_list
+
+if __name__ == '__main__':
+  color_1 = 'red'
+  color_2 = 'green'
+  words = word_tokenize(sentence)
+  word_num = len(words)
+  generate(words, score_list, "sple.tex", color_1, color_2)
+
+