| 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) | |