for regr in regrs:
regr.fit(X_train, Y_train)
predict = regr.predict(X_test)
np.seterr(invalid="ignore")
true, pred = Y_test, predict
MAE = mean_absolute_error(np.array(true), np.array(pred))
MSE = mean_squared_error(np.array(true), np.array(pred))
Pearson_r = pearsonr(np.array(true), np.array(pred))
decimal = 4
print("|%s|%s|%s|" % (round(MAE, decimal), round(MSE, decimal), round(Pearson_r[0], decimal)))
if __name__ == "__main__":
words, valence, arousal = load_anew("./resources/Lexicon/ANEW.txt")
remove_idx = []
for i, w in enumerate(words):
if w in {"glamour", "skijump"}:
remove_idx.append(i)
for i in remove_idx[::-1]:
words.pop(i)
valence.pop(i)
arousal.pop(i)
# for i,j,k in zip(words, valence, arousal):
# print(i,j,k)
vecs = build_amended_anew_vectors(words)
print(vecs.shape)
regression(vecs, np.array(arousal))
vocab[word] += 1
print(len(vocab))
return vocab
def process(corpus):
return [clean_str(sent) for sent in corpus]
vec_dim = 300
############################################## all ###############################################
corpus, ratings = load_vader(['tweets', 'movie_reviews', 'product_reviews', 'news_articles'])
# corpus, ratings = load_vader(['movie_reviews'])
# # name: tweets, movie_reviews, product_reviews, news_articles
lexicon_name = get_file_path('anew')
words, valences, _ = load_anew(lexicon_name)
corpus, ratings = screen_data(corpus, ratings, words)
ratings = np.array(ratings) + np.ones(len(ratings), dtype=float) * 5
print(len(corpus), len(corpus))
vocab = get_vocab(corpus)
# dump_picle(vocab, './data/corpus/vader/vocab_moview_reviews.p')
# vocab = load_pickle('./data/corpus/vader/vocab_moview_reviews.p')
print(len(vocab))
W, word_idx_map = build_embedding_matrix(load_embeddings('google_news'), vocab, k=300)
# dump_picle(word_idx_map, './data/corpus/vader/word_idx_map_movie_reviews.p')
# print('dump word_idx_map successful')
dump_picle(W, './data/corpus/vader/embedding_matrix_all.p')
print('dump embedding matrix file OK')
c
idx_data = make_idx_data(corpus, word_idx_map, max_len=200, kernel_size=5)
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun='Ridge_Regression')
if __name__ == '__main__':
from load_data import load_vader
normalize = True
corpus, ratings = load_vader(['movie_reviews'])
corpus = process(corpus)
# lexicon = load_lexicon(get_file_path('lexicon'))
from load_data import load_anew
from file_name import get_file_path
import numpy as np
words, valences, _ = load_anew(get_file_path('anew'))
mark = np.array(ratings) + np.ones(len(ratings), dtype=float) * 5
lexicon = dict()
for i, word in enumerate(words):
lexicon[word] = valences[i]
# # the following could use to check the same words in corpus and lexicon
# from visualization import show_common_term
# show_common_term(corpus, lexicon)
# exit()
# valence_mean, valence_true = linear_fusion(corpus, lexicon, mark)
# print('start.....')
# cv(valence_mean, valence_true, multivariant=False)
# print('OK')
def generate_anew_synsets_data():
anew_words, _, _ = load_anew('./resource/ANEW.txt')
build_synsets(anew_words)
print('Saved.')
print(anew_words)
print(len(vocab))
return vocab
def process(corpus):
return [clean_str(sent) for sent in corpus]
vec_dim = 300
############################################## all ###############################################
corpus, ratings = load_vader(
['tweets', 'movie_reviews', 'product_reviews', 'news_articles'])
# corpus, ratings = load_vader(['movie_reviews'])
# # name: tweets, movie_reviews, product_reviews, news_articles
lexicon_name = get_file_path('anew')
words, valences, _ = load_anew(lexicon_name)
corpus, ratings = screen_data(corpus, ratings, words)
ratings = np.array(ratings) + np.ones(len(ratings), dtype=float) * 5
print(len(corpus), len(corpus))
vocab = get_vocab(corpus)
# dump_picle(vocab, './data/corpus/vader/vocab_moview_reviews.p')
# vocab = load_pickle('./data/corpus/vader/vocab_moview_reviews.p')
print(len(vocab))
W, word_idx_map = build_embedding_matrix(load_embeddings('google_news'),
vocab,
k=300)
# dump_picle(word_idx_map, './data/corpus/vader/word_idx_map_movie_reviews.p')
# print('dump word_idx_map successful')
dump_picle(W, './data/corpus/vader/embedding_matrix_all.p')
print('dump embedding matrix file OK')
occur_times = (word_count >= 1).sum()
if occur_times > 0:
avg = np.average(np.array(valence)[word_count >= 1])
else:
avg = -1
texts_scores.append(avg)
return texts_scores
if __name__ == '__main__':
from load_data import load_selected_data, load_anew, load_extend_anew
# # print(count_matching(texts, words))
texts, labels = load_selected_data(data_type='train', stem=False)
words, valence, _ = load_anew()
# select_matching(texts,labels, words)
# exit()
words, valence = np.array(words), np.array(valence)
words_pos, valence_pos = words[valence > np.average(valence)], valence[
valence > np.average(valence)] # avg = 5.1511713456
words_neg, valence_neg = words[valence < np.average(valence)], valence[
valence < np.average(valence)] # avg = 5.1511713456
pos = avg_valence(texts, words_pos, valence_pos)
neg = avg_valence(texts, words_neg, valence_neg)
from visualization import draw_scatter_with_color
draw_scatter_with_color(pos, neg, labels, 'pos', 'neg')
__author__ = 'NLP-PC' from load_data import load_anew from visualization import draw_scatter_with_labels from load_data import load_extend_anew words, arousal, valence = load_extend_anew() draw_scatter_with_labels(arousal,valence, words, 'arousal', 'valence') word, a, v = load_anew() draw_scatter_with_labels(a, v, word, 'arousal', 'valence')
if occur_times > 0:
avg = np.average(np.array(valence)[word_count >= 1])
else:
avg = -1
texts_scores.append(avg)
return texts_scores
if __name__ == '__main__':
from load_data import load_selected_data, load_anew, load_extend_anew
# # print(count_matching(texts, words))
texts, labels = load_selected_data(data_type='train', stem=False)
words, valence, _ = load_anew()
# select_matching(texts,labels, words)
# exit()
words, valence = np.array(words), np.array(valence)
words_pos, valence_pos = words[valence > np.average(valence)], valence[
valence > np.average(valence)] # avg = 5.1511713456
words_neg, valence_neg = words[valence < np.average(valence)], valence[
valence < np.average(valence)] # avg = 5.1511713456
pos = avg_valence(texts, words_pos, valence_pos)
neg = avg_valence(texts, words_neg, valence_neg)
from visualization import draw_scatter_with_color
draw_scatter_with_color(pos, neg, labels, 'pos', 'neg')
cost_fun='Ridge_Regression')
if __name__ == '__main__':
from load_data import load_vader
normalize = True
corpus, ratings = load_vader(['movie_reviews'])
corpus = process(corpus)
# lexicon = load_lexicon(get_file_path('lexicon'))
from load_data import load_anew
from file_name import get_file_path
import numpy as np
words, valences, _ = load_anew(get_file_path('anew'))
mark = np.array(ratings) + np.ones(len(ratings), dtype=float) * 5
lexicon = dict()
for i, word in enumerate(words):
lexicon[word] = valences[i]
# # the following could use to check the same words in corpus and lexicon
# from visualization import show_common_term
# show_common_term(corpus, lexicon)
# exit()
# valence_mean, valence_true = linear_fusion(corpus, lexicon, mark)
# print('start.....')
# cv(valence_mean, valence_true, multivariant=False)
# print('OK')
__author__ = "NLP-PC" from load_data import load_anew from visualization import draw_scatter_with_labels from load_data import load_extend_anew words, arousal, valence = load_extend_anew() draw_scatter_with_labels(arousal, valence, words, "arousal", "valence") word, a, v = load_anew() draw_scatter_with_labels(a, v, word, "arousal", "valence")
def generate_anew_synsets_data():
anew_words,_,_ = load_anew('./resource/ANEW.txt')
build_synsets(anew_words)
print('Saved.')
print(anew_words)
regr.fit(X_train, Y_train)
predict = regr.predict(X_test)
np.seterr(invalid='ignore')
true, pred = Y_test, predict
MAE = mean_absolute_error(np.array(true), np.array(pred))
MSE = mean_squared_error(np.array(true), np.array(pred))
Pearson_r = pearsonr(np.array(true), np.array(pred))
decimal = 4
print('|%s|%s|%s|' % (round(MAE, decimal), round(
MSE, decimal), round(Pearson_r[0], decimal)))
if __name__ == '__main__':
words, valence, arousal = load_anew('./resources/Lexicon/ANEW.txt')
remove_idx = []
for i, w in enumerate(words):
if w in {'glamour', 'skijump'}:
remove_idx.append(i)
for i in remove_idx[::-1]:
words.pop(i)
valence.pop(i)
arousal.pop(i)
# for i,j,k in zip(words, valence, arousal):
# print(i,j,k)
vecs = build_ori_anew_vectors(words)
print(vecs.shape)
print("Valence")
regression(vecs, np.array(valence))
__author__ = "NLP-PC"
# coding: utf-8
import goslate
from load_data import load_lexicon
from file_name import get_file_path
from load_data import load_anew
import os
os.chdir("..")
print(load_lexicon(get_file_path("lexicon")))
words, valence, arousal = load_anew(get_file_path("anew"))
gs = goslate.Goslate()
for tw_text in gs.translate(words, "zh-tw"):
print(tw_text)
print(gs.translate("Hi", "zh-TW"))
# You could get all supported language list through get_languages
languages = gs.get_languages()
print(languages["zh-TW"])
__author__ = 'NLP-PC'
# coding: utf-8
import goslate
from load_data import load_lexicon
from file_name import get_file_path
from load_data import load_anew
import os
os.chdir('..')
print(load_lexicon(get_file_path('lexicon')))
words, valence, arousal = load_anew(get_file_path('anew'))
gs = goslate.Goslate()
for tw_text in gs.translate(words, 'zh-tw'):
print(tw_text)
print(gs.translate('Hi', 'zh-TW'))
# You could get all supported language list through get_languages
languages = gs.get_languages()
print(languages['zh-TW'])
