def store_name_brand_attribute_features(filepath, sheetnum, colnum, data, storepath): data = tp.seg_fil_excel(filepath, sheetnum, colnum) n_b_a = name_brand_attribute(data) # Need initiallized f = open(storepath, 'w') for i in n_b_a: f.write(str(i[0])+' '+str(i[1])+' '+str(i[2])+'\n') f.close()
def store_name_brand_attribute_features(filepath, sheetnum, colnum, data, storepath): data = tp.seg_fil_excel(filepath, sheetnum, colnum) n_b_a = name_brand_attribute(data) # Need initiallized f = open(storepath, 'w') for i in n_b_a: f.write(str(i[0])+' '+str(i[1])+' '+str(i[2])+'\n') f.close()
def store_name_brand_attribute_features(review_data, filepath, sheetnum,
colnum, storepath):
lm = NgramModel(1, review_data, estimator=None)
data = tp.seg_fil_excel(filepath, sheetnum, colnum)
ep = entropy_perplexity(lm, data)
p = open(storepath, 'w')
for j in ep:
p.write(str(j[0]) + '\t' + str(j[1]) + '\n')
p.close()
def store_adj_adv_v_num_feature(filepath, sheetnum, colnum, data, storepath):
data = tp.seg_fil_excel(filepath, sheetnum, colnum)
adj_adv_num = count_adj_adv(data)
f = open(storepath, "w")
for i in adj_adv_num:
f.write(str(i[0]) + " " + str(i[1]) + " " + str(i[2]) + "\n")
f.close()
def store_word_sent_num_features(filepath, sheetnum, colnum, data, storepath):
data = tp.seg_fil_excel(filepath, sheetnum, colnum)
word_sent_num = word_sent_count(data) # Need initiallized
f = open(storepath,'w')
for i in word_sent_num:
f.write(str(i[0])+' '+str(i[1])+' '+str(i[2])+'\n')
f.close()
def store_word_sent_num_features(filepath, sheetnum, colnum, data, storepath):
data = tp.seg_fil_excel(filepath, sheetnum, colnum)
word_sent_num = word_sent_count(data) # Need initiallized
f = open(storepath, "w")
for i in word_sent_num:
f.write(str(i[0]) + " " + str(i[1]) + " " + str(i[2]) + "\n")
f.close()
def store_adj_adv_v_num_feature(filepath, sheetnum, colnum, data, storepath):
data = tp.seg_fil_excel(filepath, sheetnum, colnum)
adj_adv_num = count_adj_adv(data)
f = open(storepath, 'w')
for i in adj_adv_num:
f.write(str(i[0]) + ' ' + str(i[1]) + ' ' + str(i[2]) + '\n')
f.close()
def store_seg_fil_result(filepath, sheetnum, colnum, storepath):
# Read excel file of review and segmention and filter stopwords
seg_fil_result = tp.seg_fil_excel(filepath, sheetnum, colnum)
# Store filtered reviews
fil_file = open(storepath, "w")
for sent in seg_fil_result:
for word in sent:
fil_file.write(word.encode("utf8") + " ")
fil_file.write("\n")
fil_file.close()
def store_seg_fil_result(filepath, sheetnum, colnum, storepath):
# Read excel file of review and segmention and filter stopwords
seg_fil_result = tp.seg_fil_excel(filepath, sheetnum, colnum)
# Store filtered reviews
fil_file = open(storepath, 'w')
for sent in seg_fil_result:
for word in sent:
fil_file.write(word.encode('utf8') + ' ')
fil_file.write('\n')
fil_file.close()
def store_name_brand_attribute_features(review_data, filepath, sheetnum, colnum, storepath): # Building an ngram language model of a certain product category review lm = NgramModel(1, review_data, estimator=None) # Need initiallized # Read full review dataset data = tp.seg_fil_excel(filepath, sheetnum, colnum) ep = entropy_perplexity(lm, data) p = open(storepath,'w') for j in ep: p.write(str(j[0]) + '\t' + str(j[1]) + '\n') p.close()
def store_name_brand_attribute_features(review_data, filepath, sheetnum,
colnum, storepath):
# Building an ngram language model of a certain product category review
lm = NgramModel(1, review_data, estimator=None) # Need initiallized
# Read full review dataset
data = tp.seg_fil_excel(filepath, sheetnum, colnum)
ep = entropy_perplexity(lm, data)
p = open(storepath, 'w')
for j in ep:
p.write(str(j[0]) + '\t' + str(j[1]) + '\n')
p.close()
ent_per = []
for r in dataset:
ent = model.entropy(r)
per = model.perplexity(r)
ent_per.append((ent, per))
return ent_per
"""
testing
"""
filepath = 'E:/GraduationProject/pythoncode/project/Prediction/main/ReviewSet/Motorala.xlsx'
storepath = 'E:/GraduationProject/pythoncode/project/Prediction/main/result/DifferenceFeature.txt'
review = word_by_word_review(filepath, 1, 11)
data = tp.seg_fil_excel(filepath, 1, 11)
# print len_data
# lm = NgramModel(1, review, estimator=None)
# ep = entropy_perplexity(lm,data[1:990])
# print ep
# Store features
# def store_name_brand_attribute_features(review_data, filepath, sheetnum, colnum, storepath):
# # Building an ngram language model of a certain product category review
# lm = NgramModel(1, review_data, estimator=None) # Need initiallized
#
# # Read full review dataset
# data = tp.seg_fil_excel(filepath, sheetnum, colnum)
#
# ep = entropy_perplexity(lm, data)
#