from load_data import load_CVAW
from load_data import load_NTUSD
def save_list(filename, list):
with open(filename,'w',encoding='utf-8') as f:
for l in list:
if l != "":
f.write(l+"\n")
lexicon_data = load_CVAW()
cvaw_words = [line[0] for line in lexicon_data]
print('The words in CVAW lexicons: %s' % str(cvaw_words))
NTUSD_positive_words = load_NTUSD('./resources/ntusd-positive (zh-tw).txt')
NTUSD_negative_words = load_NTUSD('./resources/ntusd-negative (zh-tw).txt')
print('NTUSD')
print("Positive: %s"%str(NTUSD_positive_words))
print("Negative: %s"%str(NTUSD_negative_words))
print('same words in ntusd_postive')
print(sorted(list(set(NTUSD_positive_words) & set(cvaw_words))))
print('same words in ntusd_negative')
print(sorted(list(set(NTUSD_negative_words) & set(cvaw_words))))
common_words = sorted(list((set(NTUSD_positive_words) & set(cvaw_words)) | (set(NTUSD_negative_words) & set(cvaw_words))))
ntusd_p = (sorted(list(set(NTUSD_positive_words) - set(cvaw_words))))
ntusd_n = (sorted(list(set(NTUSD_negative_words) - set(cvaw_words))))
not_in_CVAW = sorted(list((set(NTUSD_positive_words) - set(cvaw_words)) | (set(NTUSD_negative_words) - set(cvaw_words))))
save_list('./resources/common_words.txt', common_words)
save_list('./resources/different_words.txt', not_in_CVAW)
save_list('./resources/NTUSD_p.txt', ntusd_p)
print('The predicted values is (using Geometric Average): %s'% predicted_value_g)
geometric.append(predicted_value_g)
print('The true values is: %s' % true_values[i])
return arithmetic, geometric
if __name__ == '__main__':
########################################### Hyper-parameters ###########################################
target = 'arousal' # values: "valence", "arousal"
categorical = 'all' # values: 'all', "book", "car", "laptop", "hotel", "news", "political"
########################################################################################################
# texts, valence, arousal = read_mix_data(categorical)
from load_data import load_CVAT_3
# texts, valence, arousal = load_CVAT_3('./resources/corpus 2009 sigma 1.5.csv','./resources/tokenized_texts.p', categorical=categorical)
texts, valence, arousal = load_CVAT_3('./resources/CVAT (utf-8).csv','./resources/tokenized_texts_(newest3.31).p', categorical=categorical)
lexicon = load_CVAW()
d = dict()
if target == 'valence':
ind = 1
true_values = valence
print('Valence prediction...')
elif target == 'arousal':
ind = 2
true_values = arousal
print('Arousal preddiction...')
for l in lexicon:
d[l[0]] = l[ind]
arithmetic, geometric = va_prediction(texts, d, true_values)
print('Prediction result (arithmetic average):')
regression_evaluate(true_values, arithmetic)
# texts, valence, arousal = load_CVAT_2("./resources/valence_arousal(sigma=1.5).csv", categorical=categorical)
from load_data import load_CVAT_3
# texts, valence, arousal = load_CVAT_3('./resources/corpus 2009 sigma 1.5.csv','./resources/tokenized_texts.p', categorical=categorical)
# texts, valence, arousal = load_CVAT_3('./resources/valence_arousal(sigma=1.5).csv','./resources/tokenized_texts_(old).p', categorical=categorical)
from mix_data import read_mix_data
texts, valence, arousal = read_mix_data(categorical)
if option == 'V':
Y = valence
elif option == 'A':
Y = arousal
else:
raise Exception('Wrong parameters!')
lexicon = load_CVAW(extended=using_extended_lexicon)
d = dict()
ind = 1 if option == 'V' else 2
for l in lexicon:
d[l[0]] = l[ind]
predicted_ratings = mean_ratings(texts, d, mean_method, Y)
print(predicted_ratings)
print(Y)
out = regression_evaluate(Y, predicted_ratings)
draw_scatter(Y, predicted_ratings, 'True Values', 'Predicted Values', title='Scatter')
out2 = cv(predicted_ratings, Y)
Dims = 'Valence' if option == 'V' else 'Arousal'
# texts, valence, arousal = load_CVAT_2("./resources/valence_arousal(sigma=1.5).csv", categorical=categorical)
from load_data import load_CVAT_3
# texts, valence, arousal = load_CVAT_3('./resources/corpus 2009 sigma 1.5.csv','./resources/tokenized_texts.p', categorical=categorical)
# texts, valence, arousal = load_CVAT_3('./resources/valence_arousal(sigma=1.5).csv','./resources/tokenized_texts_(old).p', categorical=categorical)
from mix_data import read_mix_data
texts, valence, arousal = read_mix_data(categorical)
if option == 'V':
Y = valence
elif option == 'A':
Y = arousal
else:
raise Exception('Wrong parameters!')
lexicon = load_CVAW(extended=using_extended_lexicon)
d = dict()
ind = 1 if option == 'V' else 2
for l in lexicon:
d[l[0]] = l[ind]
predicted_ratings = mean_ratings(texts, d, mean_method, Y)
print(predicted_ratings)
print(Y)
out = regression_evaluate(Y, predicted_ratings)
draw_scatter(Y,
predicted_ratings,
'True Values',
'Predicted Values',
title='Scatter')
from load_data import load_CVAW
from load_data import load_NTUSD
def save_list(filename, list):
with open(filename, 'w', encoding='utf-8') as f:
for l in list:
if l != "":
f.write(l + "\n")
lexicon_data = load_CVAW()
cvaw_words = [line[0] for line in lexicon_data]
print('The words in CVAW lexicons: %s' % str(cvaw_words))
NTUSD_positive_words = load_NTUSD('./resources/ntusd-positive (zh-tw).txt')
NTUSD_negative_words = load_NTUSD('./resources/ntusd-negative (zh-tw).txt')
print('NTUSD')
print("Positive: %s" % str(NTUSD_positive_words))
print("Negative: %s" % str(NTUSD_negative_words))
print('same words in ntusd_postive')
print(sorted(list(set(NTUSD_positive_words) & set(cvaw_words))))
print('same words in ntusd_negative')
print(sorted(list(set(NTUSD_negative_words) & set(cvaw_words))))
common_words = sorted(
list((set(NTUSD_positive_words) & set(cvaw_words))
| (set(NTUSD_negative_words) & set(cvaw_words))))
ntusd_p = (sorted(list(set(NTUSD_positive_words) - set(cvaw_words))))
ntusd_n = (sorted(list(set(NTUSD_negative_words) - set(cvaw_words))))
not_in_CVAW = sorted(
if __name__ == '__main__':
########################################### Hyper-parameters ###########################################
target = 'arousal' # values: "valence", "arousal"
categorical = 'all' # values: 'all', "book", "car", "laptop", "hotel", "news", "political"
########################################################################################################
# texts, valence, arousal = read_mix_data(categorical)
from load_data import load_CVAT_3
# texts, valence, arousal = load_CVAT_3('./resources/corpus 2009 sigma 1.5.csv','./resources/tokenized_texts.p', categorical=categorical)
texts, valence, arousal = load_CVAT_3(
'./resources/CVAT (utf-8).csv',
'./resources/tokenized_texts_(newest3.31).p',
categorical=categorical)
lexicon = load_CVAW()
d = dict()
if target == 'valence':
ind = 1
true_values = valence
print('Valence prediction...')
elif target == 'arousal':
ind = 2
true_values = arousal
print('Arousal preddiction...')
for l in lexicon:
d[l[0]] = l[ind]
arithmetic, geometric = va_prediction(texts, d, true_values)
print('Prediction result (arithmetic average):')
regression_evaluate(true_values, arithmetic)
