def seg_filter_txt(filepath, storepath):
txtfile = open(filepath, 'r')
txtdata = txtfile.readlines()
txtfile.close()
review_data = tp.segmentation(txtdata[0], 'list')
stopfile = open('D:/code/seg_fil_test/stopword.txt', 'r')
stopdata1 = stopfile.readlines()
stopdata2 = ''.join(stopdata1)
stopwords = stopdata2.decode('utf8').split('\n')
stopfile.close()
seg_fil_result = []
for review in review_data:
fil = [
word for word in review if word not in stopwords and word != ' '
]
seg_fil_result.append(fil)
fil = []
fil_file = open(storepath, 'w')
for word in seg_fil_result:
fil_file.write(word.encode('utf8') + ' ')
fil_file.close()
def is_single_review_sentiment(review): cuted_review = tp.cut_sentence_2(review)# 将评论切割成句子 for sent in cuted_review: seg_sent = tp.segmentation(sent, 'list')# 将句子做分词处理 for word in seg_sent: if word in sentiment_dict: return True return False
def word_sent_count(dataset):
word_sent_count = []
for review in dataset:
sents = tp.cut_sentence_2(review) # 切割成句子
words = tp.segmentation(review, 'list') #切割成词语
sent_num = len(sents)
word_num = len(words)
sent_word = float(word_num) / float(
sent_num
) # review length = word number/sentence number 也即每个句子平均含有词语数量
word_sent_count.append([word_num, sent_num, sent_word])
return word_sent_count
def word_sent_count(rawData):
begin=time.clock()
word_sent_count = []
for review in rawData:
sents = tp.cut_sentence_2(review)# 切割成句子
words = tp.segmentation(review,'list')#切割成词语
sent_num = len(sents)
word_num = len(words)
sent_word = float(word_num)/float(sent_num) # review length = word number/sentence number 也即每个句子平均含有词语数量
word_sent_count.append([word_num, sent_num, sent_word])
end=time.clock()
print 'extract word_sent_count feature time is:', end - begin, 'handle data item num is:', len(rawData)
return word_sent_count
def sentence_sentiment_score(dataset):
cuted_review = []
for cell in dataset:
cuted_review.append(tp.cut_sentence_2(cell))
all_review_count = []
for review in cuted_review:
single_review_count = []
if len(review) == 0: #出现空行时
single_review_count.append(transform_to_positive_num(0, 0))
for sent in review:
seg_sent = tp.segmentation(sent, 'list')
i = 0 #word position counter
a = 0 #sentiment word position
poscount = 0 #count a pos word
negcount = 0
for word in seg_sent:
if word in posdict:
poscount += 1
for w in seg_sent[a:i]:
poscount = match(w, poscount)
a = i + 1
elif word in negdict:
negcount += 1
for w in seg_sent[a:i]:
negcount = match(w, negcount)
a = i + 1
elif word == '！'.decode('utf8') or word == '!'.decode('utf8'):
for w2 in seg_sent[::-1]:
if w2 in posdict:
poscount += 2
break
elif w2 in negdict:
negcount += 2
break
i += 1
single_review_count.append(
transform_to_positive_num(
poscount, negcount)) #[[s1_score], [s2_score], ...]
all_review_count.append(
single_review_count
) # [[[s11_score], [s12_score], ...], [[s21_score], [s22_score], ...], ...]
return all_review_count
def word_by_word_review(filepath, sheetnum, colnum):
# Read product review data from excel file and segment every review
review_data = []
for cell in tp.get_excel_data(filepath, sheetnum, colnum, 'data')[0:tp.get_excel_data(filepath, sheetnum, colnum, 'rownum')]:
review_data.append(tp.segmentation(cell, 'list')) # Seg every reivew
# Read txt file contain stopwords
stopwords = tp.get_txt_data('D:/ReviewHelpfulnessPrediction\PreprocessingModule/stopword.txt', 'lines')
# Filter stopwords from reviews
seg_fil_result = []
for review in review_data:
fil = [word for word in review if word not in stopwords and word != ' ']
seg_fil_result.append(fil)
fil = []
# Return review set as onedimentional list
review = list(itertools.chain(*seg_fil_result))
return review
def single_review_sentiment_score(review):
single_review_senti_score = []
cuted_review = tp.cut_sentence_2(review) # 将评论切割成句子
for sent in cuted_review:
seg_sent = tp.segmentation(sent, 'list') # 将句子做分词处理
i = 0 # word position counter
s = 0 # sentiment word position
poscount = 0 # count a positive word
negcount = 0 # count a negative word
for word in seg_sent:
if word in posdict:
poscount += 1
for w in seg_sent[s:i]:
poscount = match(w, poscount)
s = i + 1 # a是什么
elif word in negdict:
negcount += 1
for w in seg_sent[s:i]:
negcount = match(w, negcount)
s = i + 1 # a是什么
# Match "!" in the review, every "!" has a weight of +2 ！强调句子情感
elif word == "！".decode('utf8') or word == "!".decode('utf8'):
for w2 in seg_sent[::-1]:
if w2 in posdict:
poscount += 2
break
elif w2 in negdict:
negcount += 2
break
i += 1
single_review_senti_score.append(
transform_to_positive_num(poscount, negcount))
#print(sumup_sentence_sentiment_score(single_review_senti_score))
review_sentiment_score = sumup_sentence_sentiment_score(
single_review_senti_score)
return review_sentiment_score
