VADER-With-heatmaps

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)