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这50个特征有很多是一类，比如soup和sandwich，都表示的餐饮中的一个菜，那么未来能否使用wiki大规模语料库将这些近义词合并成一个类目会成为一个比较大的工作量；再有就是挖出来的feature都是一个词，看看能不能挖出一个短语吧。
（四）源码
# coding:utf-8
import nltk,os,sys
#
# 进度条
#
def View_Bar(flag,sum):
rate = float(flag) / sum
rate_num = rate * 100
if flag % 15.0 == 0:
print 'r%.2f%%: ' %(rate_num),# r%.2f后面跟的两个百分号会输出一个'%'
sys.stdout.flush()
#
# 筛选 指定类型的 店家
#
def Filter_Business(path,type): # path是business表路径，type:待筛选店家的categories
lines = open(path,'r').readlines()
filter_bussiness_id = {} # 筛选完成的商家id的字典 {"5UmKMjUEUNdYWqANhGckJw":None,"UsFtqoBl7naz8AVUBZMjQQ":None,}
flag = 0 # 进度条
for line in lines:
line = line.split('"categories": ')
categories = line[1].split(',"city"')[0] # ["Fast Food","Restaurants"]
business_id = line[0].split(',"full_address"')[0].split('"business_id": ')[1]
if type in categories:
filter_bussiness_id[business_id[1:-1]] = 0
flag += 1
print ('筛选数据进度: ' + str(flag/85901.0))
print ('字典: ' + str(filter_bussiness_id))
print ('字典长度: ' + str(len(filter_bussiness_id)))
return filter_bussiness_id
#
# 提取指定类型店家的 review
#
def Filter_Business_Review(path,filter_business_id): # path是review文件路径
lines = open(path,'r').readlines()
f = open('/Users/John/Desktop/yelp_dataset_challenge_academic_dataset/review_text.txt','w')
flag = 0 # 进度条
for line in lines:
dict = eval(line)
business_id = dict['business_id'] # 该评论对应的商家
if filter_business_id.has_key(business_id) == True:
text = dict['text'].replace('n','')
f.write(text + 'n')
flag += 1
print (flag/2685066.0)
return 0
# #
# 词性标注 #
# #
def Tag_Word(path): # path 是所有用户的评论文件路径
lines = open(path,'r').readlines()
tags = [] # 保存每个文章分词后的词性 [ [('Excellent','JJ'),('food','NN'),('.','.')],# [('Superb','NNP'),('customer',('service','.')] ]
feature_word = [] # 提出的服务价值分布特征
# 分词、赋词性
f = open('/Users/John/Desktop/yelp_dataset_challenge_academic_dataset/word_tagged_sentences.txt','w') # 保存一下词性标注后的结果
flag = 0 # 进度条
for text in lines:
sentences = nltk.sent_tokenize(text) # 将文本拆分成句子列表
# 先对每个句子进行分词，在对这个句子进行词性标注（这样效果比较好）
for sentence in sentences:
word = nltk.word_tokenize(sentence) # 先对句子进行分词 ['Excellent','food','.']
word_tagged = nltk.pos_tag(word) # 再对这个分好的句子进行词性标注 [('Excellent','.')]
for item in word_tagged: # 将标注好的词写入文件中
f.write(item[0] + '/' + item[1] + ' ') # 'Excellent/JJ food/NN ./. '
f.write('n') # 这里我认为每个能展现feature的评论都是蕴含在一句话中的，因此每句话一行，到时候找feature的时候也是一行一行的去找
flag += 1
print ('分词进度: ' + str(flag/2687201.0))
return 0
# #
# 筛选 feature 词汇 #
# #
def Featuer_Word(path,window): # path 是词性标注后的评论句子
flag = 0 # 进度条
lines = open(path,'r').readlines()
len_lines = float(len(lines))
tagged_sentences = [] # 保存所有标注好的句子
# [ [(“'Excellent',# [('Superb','.')] ]
feature_list = [] # 挖到的feature
# 设置一个滑窗，寻找距离这个滑窗最近的一个NN、NNS
def Slip_Window_Func(tagged_sentence,i,window):
len_sentence = len(tagged_sentence)
feature = ''
k = 1
while k <= window: # 同时向目标词两边找 NNNNS
if i-k >= 0:
if tagged_sentence[i-k][1] == ('NN' or 'NNS'):
feature = tagged_sentence[i - k][0]
if i+k < len_sentence:
if tagged_sentence[i+k][1] == ('NN' or 'NNS'):
feature = tagged_sentence[i + k][0]
if feature == '':
k += 1
continue
else:
break
return feature
# 数据预处理
flag = 0 # 进度条
print ('数据预处理进度: ')
for line in lines: # 预处理一下字符串 'Excellent/JJ food/NN ./. n'
sentence = line[:-3].split(' ') # ['Excellent/JJ','food/NN','./.']
tagged_sentence = [] # 标注好的一个句子 [('Excellent','.')]
for item in sentence:
tagged_sentence.append(item.split('/'))
tagged_sentences.append(tagged_sentence)
flag += 1
View_Bar(flag,len_lines)
# if flag == 100:
# break
print('')
# 使用滑窗window确定 feature
flag = 0 # 进度条
print ('feature挖掘进度: ')
for tagged_sentence in tagged_sentences:
for i,tagged_word in enumerate(tagged_sentence): # ('Excellent','JJ')
if tagged_word[1] == ('JJ' or 'JJR' or 'JJS'): # 如果遇到形容词、比较级、最高级的话
feature = Slip_Window_Func(tagged_sentence,5) # 设置一个滑窗，寻找距离这个滑窗最近的一个NN、NNS
if feature != '' and feature_list != []: # 如果挖到了feature的话
if feature != feature_list[-1]: # 这一步是防止挖到有滑窗交集的feature
feature_list.append(feature)
elif feature != '' and feature_list == []:
feature_list.append(feature)
else:
continue
flag += 1
View_Bar(flag,len_lines)
print ('所有的feature:')
print (feature_list)
# 将feature词汇保存一下
f = open('/Users/John/Desktop/yelp_dataset_challenge_academic_dataset/feature.txt','w')
for item in feature_list:
f.write(str(item) + 'n')
print('feature词汇保存完毕')
#
# 对 feature 词汇进行再清洗
#
def Feature_Data_Cleaning(path): # path是装有feature词汇的文件路径
lines = open(path,'r').readlines()
feature_dict = {} # 保存feature的字典
# 把原始文件放到字典中
for feature in lines:
feature = feature[:-1]
if feature_dict.has_key(feature) == False: # 如果字典里没有这个feature
feature_dict[feature] = 1 # 赋一下key-value对
else: # 如果有这个feature
feature_dict[feature] += 1
# 对字典排序
feature_dict = sorted(feature_dict.iteritems(),key=lambda asd:asd[1],reverse=True) # 对value进行降序排序
print ('原始feature数目: ' + str(len(lines)))
print ('放到dict中的数目：' + str(len(feature_dict)))
# 将feature字典保存成文件
f = open('/Users/John/Desktop/yelp_dataset_challenge_academic_dataset/feature_dict.txt','w')
for item in feature_dict:
f.write(str(item) + 'n')
return 0
# 只筛选 餐厅 类型的服务行业
# filter_business_id = Filter_Business('/Users/John/Desktop/yelp_dataset_challenge_academic_dataset/yelp_academic_dataset_business.json',"Restaurants")
# 保存review
# Filter_Business_Review('/Users/John/Desktop/yelp_dataset_challenge_academic_dataset/yelp_academic_dataset_review.json',filter_business_id)
# 词性标注
# Tag_Word('/Users/John/Desktop/yelp_dataset_challenge_academic_dataset/review_text.txt')
# 筛选feature词汇
# Featuer_Word('/Users/John/Desktop/yelp_dataset_challenge_academic_dataset/word_tagged_sentences.txt',window=5)
# 对feature自会进行在清洗
Feature_Data_Cleaning('/Users/John/Desktop/yelp_dataset_challenge_academic_dataset/feature.txt')
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