def getAnswerKeys(text_set, api_key):
keys = []
nlp = BosonNLP(api_key)
result = nlp.extract_keywords(text_set, top_k=30)
for weight, word in result:
keys.append((word, int(weight * 10)))
return keys
def getAnswerKeys (text_set, api_key): keys = [] nlp = BosonNLP(api_key) result = nlp.extract_keywords(text_set, top_k=30) for weight, word in result: keys.append((word, int(weight*10))) return keys
def getKeyWords(command):
nlp = BosonNLP("IrtCRUKX.4360.giOuq49VR3V-")
r = nlp.extract_keywords(command, top_k=3)
l = []
for k, v in r:
v = v.encode('utf8')
l.append(v)
return l
def getKeyWords(command):
nlp = BosonNLP("IrtCRUKX.4360.giOuq49VR3V-")
r = nlp.extract_keywords(command, top_k=3)
l = []
for k, v in r:
v = v.encode("utf8")
l.append(v)
return l
def getKeyWords(command):
nlp = BosonNLP("ofW2OZMI.4712.UzT0VvLGGkdi")
r = nlp.extract_keywords(command, top_k=3)
l = []
for k,v in r:
v = v.encode('utf8')
l.append(v)
return l
def extract_keywords(text, top_num=10):
"""Extract Keywords."""
# 注意：在测试时请更换为您的 API token
nlp = BosonNLP('')
result = nlp.extract_keywords(text, top_k=top_num)
result_dict = {k: v for (v, k) in result}
return result_dict
def extract_keywords(text, top_num=10):
"""Extract Keywords."""
# 注意：在测试时请更换为您的 API token
nlp = BosonNLP("")
result = nlp.extract_keywords(text, top_k=top_num)
result_dict = {k: v for (v, k) in result}
return result_dict
class Scanner(object):
"""
bosonnlp 中文分词
"""
def __init__(self):
self.nlp_handler = BosonNLP(API_TOKEN_BOSONNLP)
def get_tag(self, content, remove_punctuations=False):
"""
分词后的结果，返回的是每个词的列表
"""
result = self.nlp_handler.tag(content)[0]
if remove_punctuations:
return [
x for x, y in zip(result['word'], result['tag']) if y[0] != 'w'
]
return result['word']
def get_key_word(self, content, segmented=False):
"""提取关键词"""
keywords = self.nlp_handler.extract_keywords(content, 2, segmented)
firstkey, secondkey = keywords[0], keywords[1]
return firstkey[1] if (firstkey[0] - secondkey[0]) > 0.3\
else ' '.join([firstkey[1], secondkey[1]])
def Key_word(text):
nlp = BosonNLP("x-gOGutn.27554.G6_6QvdJafES")
rest = nlp.extract_keywords(text, top_k=20)
return rest
class _BosonNLPWrapper(object):
"""
NLP object using the BosonNLP API Python SDK.
"""
news_categories = [
'physical education', 'education', 'finance', 'society',
'entertainment', 'military', 'domestic', 'science and technology',
'the internet', 'real estate', 'international', 'women', 'car', 'game'
]
def __init__(self, api_token=None):
try:
assert api_token is not None, "Please provide an API token"
except AssertionError as e:
raise
self.token = api_token
self.nlp = BosonNLP(self.token)
def get_sentiment(self, text):
pos, neg = self.nlp.sentiment(text)[0]
return {'positive': pos, 'negative': neg}
def classify_news(self, text):
numbering = range(len(_BosonNLPWrapper.news_categories))
cats_dict = dict(zip(numbering, _BosonNLPWrapper.news_categories))
clsfy_num = self.nlp.classify(text)[0]
return cats_dict[clsfy_num]
def extract_keywords(self, text, top_k=3):
result = self.nlp.extract_keywords(
text, top_k) # outputs in sorted order of weight
return [{result[i][1]: result[i][0]} for i in range(len(result))]
def segment_words_and_tag(self, text):
"""
Splits up text into segments of "words" and tags them with their respective part of speech.
See: http://docs.bosonnlp.com/tag.html
Parameters
----------
text (string): text passage to segment into separate "words" and tags them with parts of speech
Returns
-------
list of key-value pairs {word: part-of-speech-tag}
"""
result = self.nlp.tag(text)[0]
words = result['word']
tags = result['tag']
return [{words[i]: tags[i]} for i in range(len(words))]
def get_summary(self, content, title='', pct_limit=0.2):
"""
Extracts a new digest (summary) of the content.
See: http://docs.bosonnlp.com/summary.html
Parameters
----------
text (string): text passage to summarize
title (string): title of the passage (optional, may provide more accurate results)
pct_limit (float): max length of the summary in terms of percentage of the original word count
Returns
-------
string containing the summary of the passage
"""
summary = self.nlp.summary(title, content, pct_limit)
return summary
def get_similar_text(text):
BosonNLPKey = "BMRivntt.8194.5zwvLwj_ygkV"
# ZfXxO6kv.10841.LZ_TDcJiiwrl
nlp = BosonNLP(BosonNLPKey)
# 获取关键词 [[0.8391345017584958, '病毒式'], [0.3802418301341705, '蔓延']]
keyword_result = nlp.extract_keywords(text, top_k=10)
print(keyword_result)
# keyword_result = [[0.36443758441765906, '卖艺'], [0.26732109821670036, '路学院'], [0.2667011448568187, '挣钱'], [0.23801264121689353, '天目山'], [0.22618147770853975, '走时'], [0.22553389408212396, '孩子'], [0.21006863070452944, '一老一少'], [0.18830464004379927, '儿子'], [0.18298766176875855, '育才'], [0.17494405471962107, '红绿灯']]
key_word_list = []
for key_item in keyword_result:
if key_item[0] > 0.15 and len(key_item[1]) < 10:
key_word_list.append((key_item[1], key_item[0]))
conn = pymysql.connect(host='127.0.0.1',
user='******',
passwd='Ryan',
db="omdb",
charset='UTF8')
cur = conn.cursor()
vec_tfidf = []
for key in key_word_list:
sql_str = "SELECT kid from opinionmonitor_keyword WHERE word = '{0}'".format(
key[0])
cur.execute(sql_str)
for ii in cur:
vec_tfidf.append((ii[0], key[1]))
pid_list = []
for key_id in vec_tfidf:
sql_str = "SELECT pid_id from opinionmonitor_passagekeyword WHERE kid_id = {0}".format(
key_id[0])
cur.execute(sql_str)
for ii in cur:
pid_list.append(ii[0])
# 统计重复次数
pid_set = set(pid_list)
corpus_tfidf = []
p_id_list = []
for pid in pid_set:
c = pid_list.count(pid)
# 大于2才加入结果集中
if c > 0:
sql_str = "SELECT kid_id, ratio from opinionmonitor_passagekeyword WHERE pid_id = {0}".format(
pid)
cur.execute(sql_str)
kr_list = []
for ii in cur:
kr_list.append((ii[0], ii[1]))
corpus_tfidf.append(kr_list)
p_id_list.append(pid)
cur.close() #关闭游标
conn.commit() #向数据库中提交任何未解决的事务，对不支持事务的数据库不进行任何操作
conn.close() #关闭到数据库的连接，释放数据库资源
# 计算cos距离
print("比较矩阵")
print(corpus_tfidf)
print("文本向量")
print(vec_tfidf)
if corpus_tfidf == []:
return dict()
index = similarities.MatrixSimilarity(corpus_tfidf)
sims = index[vec_tfidf]
r_pid_dict = dict()
sim_result_list = list(sims)
for index, item in enumerate(sim_result_list):
if item > 0.5:
r_pid_dict[p_id_list[index]] = item
result_list = []
result_order_dict = OrderedDict()
r_pid_dict = OrderedDict(sorted(r_pid_dict.items(), key=lambda x: x[1]))
print(r_pid_dict)
for k, v in r_pid_dict.items():
passage_list = Passage.objects.filter(pid=k)
passage = passage_list[0]
passage.st = v
item_tuple = passage.to_tuple()
result_order_dict[item_tuple[0]] = item_tuple[1]
# result_list.append(passage.to_tuple())
return result_order_dict
def main():
global last_extrect_tag_time, last_extrect_tag_time
token = json.load(open('./config/token.json','r'))
if DEBUG:
client = EvernoteClient(token=token['en_token'],sandbox=True)
else:
client = EvernoteClient(token=token['en_token'])
client.service_host = 'app.yinxiang.com'
print '现在服务器是:',client._get_endpoint()
#bosonNlp
nlp = BosonNLP(token['boson_nlp_token'])
note_store = client.get_note_store()
#获取上一次同步状态
if os.path.exists(data_file('sync_state')):
last_sync_state = json.load(open(data_file('sync_state'),'r'))
last_update_count = last_sync_state['updateCount']
last_extrect_tag_time = last_sync_state['currentTime']
#获取当前同步状态
currnet_sync_state = get_current_sync_state(note_store)
if currnet_sync_state.updateCount > last_update_count:
new_updated_count = currnet_sync_state.updateCount - last_update_count
print currnet_sync_state.__dict__
new_note_filter = NoteStore.NoteFilter()
new_note_filter.order = Types.NoteSortOrder.CREATED
new_notes = note_store.findNotes(new_note_filter,0,new_updated_count)
print 'totalNumber:%d\tNoteListNum:%d' %(new_notes.totalNotes,len(new_notes.notes))
else:
print('没有新增更新...')
exit(1)
# 获取用户的所有tags
tags = Tags(note_store=note_store)
alltags = tags.tags
print '标签云:\n'
print '\t'.join(alltags.keys())
#操作新note
for note in new_notes.notes:
#如果笔记创建时间小于上次同步时间
if note.created <= last_extrect_tag_time:
continue
print '\n'+'*'*120
content = note_store.getNoteContent(note.guid)
print "guid:%s\ntitle:%s\ncreated:%s\n作者:%s" %(note.guid,note.title,note.created,note.attributes.author)
print 'author:%s\nsource:%s\nsourceURL:%s\nsourceApplication:%s' %(note.attributes.author,note.attributes.source,note.attributes.sourceURL,note.attributes.sourceApplication)
if not note.attributes.sourceURL:
continue
print "现有标签(tags):%s" %(",".join(note_store.getNoteTagNames(note.guid)))
print '-'*120
#print "内容(%d):created:%s,\n%s" %(note.contentLength,note.created,content)
#解析note xml 提取出所有的文字
try:
parser = ET.XMLParser()
parser.entity['nbsp'] = ''
parser.entity['ldquo'] = ''
parser.entity['rdquo'] = ''
parser.entity['hellip'] = ''
tree = ET.parse(StringIO(content),parser=parser)
except Exception,data:
print 'ElementTree parser error'
print content
print 'errorData:'
print data
print 'exception:'
print Exception
exit(1)
en_note = tree.findall('.')[0]
content_string = ''.join(en_note.itertext())
#写入文件
with codecs.open(note_file(note.guid),'w+',encoding='utf-8') as f:
f.write(content_string)
#通过BosonNLP 拿到文章命名实体
ner_tag_guid_list = []
ner_tag_name_list = []
ner = Ner(content_string).process(nlp)
entites = ner.collect_type_entity(count=1)
for entity in entites:
tag = tags.add(entity)
ner_tag_guid_list.append(tag.guid)
ner_tag_name_list.append(tag.name)
#通过 BosonNLP 拿到文章的关键字
extract_keywords = nlp.extract_keywords(content_string,top_k=20)
keywords = [item[1].upper() for item in extract_keywords]
print '通过 BosonNLP extract_keywords 拿到文章的前20个关键字:'
for keyword in extract_keywords:
print '%s \t %s' %(keyword[1],keyword[0])
print '-'*120
#对比 找出交集tag的guid
keywords_tag_guid_list = []
newKeyWords = []
for keyword in keywords:
if tags.exist(keyword):
existTag = tags.get(keyword)
keywords_tag_guid_list.append(existTag.guid)
newKeyWords.append(existTag.name)
print '\nextract_keywords与自己所有tag的交集:'
print '\t'.join(newKeyWords)
#追加新笔记的tags
new_tag_guid_list = list(set(keywords_tag_guid_list).union(set(ner_tag_guid_list)))
print 'extract_keywords+ner的tag:'
newKeyWords.extend(ner_tag_name_list)
print '\t'.join(newKeyWords)
if note.tagGuids:
note.tagGuids.extend(new_tag_guid_list)
else:
note.tagGuids = new_tag_guid_list
note_store.updateNote(note)
results.append(item[_attribution])
# pprint.pprint(item[_attribution])
return results
for item in _cursor:
pprint.pprint(item)
return
# Connect to mongodb
client = MongoClient("mongodb://localhost:27017/")
db = client.lagou
collection = db.lagouJob
# Find by company name
cursor = collection.find({"companyName":"深圳市贝利美维软件有限公司"})
# Find by Job Title
# cursor = collection.find({"jobTitle":{"$regex": u"区块链"}})
result = decodeResult(cursor, "jobDescription")
resultContent = "".join(result)
nlpResult = nlp.extract_keywords(resultContent, top_k=100)
f = open('companyKeywords.txt', 'w')
for weight, word in nlpResult:
outString = str(weight) + " "+ str(word) + "\n"
print(weight, word)
f.write(outString)
f.close()
i = 77
id = rlaq_u2.loc[i, 'id']
ajbh = rlaq_u2.loc[i, 'ajbh']
fssj = pd.to_datetime(rlaq_u2.loc[i, 'time'])
txt = rlaq_u2.loc[i, 'jyaq']
txt0 = txt
place = rlaq_u2.loc[i, 'place']
print(txt0)
#提取时间、地点、人物
result = nlp.ner(txt)[0]
words = result['word']
entities = result['entity']
Btime = []
Bplace = []
Bpeople = []
for entity in entities:
if entity[2] == 'time':
Btime.append(''.join(words[entity[0]:entity[1]]))
if entity[2] == 'location':
Bplace.append(''.join(words[entity[0]:entity[1]]))
if entity[2] == 'person_name':
Bpeople.append(''.join(words[entity[0]:entity[1]]))
print('时间：', ' | '.join(Btime))
print('地点：', ' | '.join(Bplace))
print('人物：', ' | '.join(Bpeople))
#提取关键词
kw = nlp.extract_keywords(txt, top_k=10)
for w, k in kw:
print('关键词：', k, ' , ', '权重：', w)
class BosonNlpp:
def __init__(self):
self.bonlp = BosonNLP('IKBIoANy.14545.A7GCYBnT9jIB')
#情感分析
def testSentiment(self, s):
result = self.bonlp.sentiment(s)
return result
#print(result)
#命名实体识别
def lexicalAnalysis(self, s):
result = self.bonlp.ner(s)[0]
return result
#依存文法分析
def textDependency(self, s):
result = self.bonlp.depparser(s)
return result
#关键词提取
def testKeywords(self, s):
result = self.bonlp.extract_keywords(s, top_k=10)
return result
#新闻分类
def textClassify(self, s):
resultlist = self.bonlp.classify(s)
classifys = {
0: '体育',
1: '教育',
2: '财经',
3: '社会',
4: '娱乐',
5: '军事',
6: '国内',
7: '科技',
8: '互联网',
9: '房产',
10: '国际',
11: '女人',
12: '汽车',
13: '游戏'
}
return (classifys[resultlist[0]])
#语义联想
def lexicalSynonym(self, term):
result = self.bonlp.suggest(term, top_k=10)
return result
#分词与词性标注
def fenci(self, s):
result = self.bonlp.tag(s)
return result
def newssubstract(self, s):
#s=s.encode('utf8')
s = s.decode('utf-8')
result = self.bonlp.summary('', s)
return result
class _BosonNLPWrapper(object):
"""
NLP object using the BosonNLP API Python SDK.
"""
news_categories = ['physical education', 'education', 'finance', 'society', 'entertainment', 'military',
'domestic', 'science and technology', 'the internet', 'real estate', 'international',
'women', 'car', 'game']
def __init__(self, api_token=None):
try:
assert api_token is not None, "Please provide an API token"
except AssertionError as e:
raise
self.token = api_token
self.nlp = BosonNLP(self.token)
def get_sentiment(self, text):
"""
Performs sentiment analysis on a text passage (works for Chinese text).
See: http://docs.bosonnlp.com/sentiment.html
Parameters
----------
text (string): text passage to be analyzed for sentiment
Returns
-------
dictionary with 'positive' and 'negative' as keys with their respective weights as values
>>> nlp = BosonNLPWrapper('')
>>> nlp.get_sentiment('不要打擾我')
{'positive': 0.3704911989140307, 'negative': 0.6295088010859693}
>>> nlp.get_sentiment('我很高興跟你見面')
{'positive': 0.856280735624867, 'negative': 0.14371926437513308}
"""
pos, neg = self.nlp.sentiment(text)[0]
return {'positive': pos, 'negative': neg}
def classify_news(self, text):
"""
Classifies news text into 14 different categories.
See: http://docs.bosonnlp.com/classify.html
Parameters
----------
text (string): text passage to classify into news categories defined in news_categories
Returns
-------
one of the 14 categories in news_categories that the text was classified into
"""
numbering = range(len(_BosonNLPWrapper.news_categories))
cats_dict = dict(zip(numbering, _BosonNLPWrapper.news_categories))
clsfy_num = self.nlp.classify(text)[0]
return cats_dict[clsfy_num]
def extract_keywords(self, text, top_k=3):
"""
Extracts the top k keywords and the weight of each word in the text.
See: http://docs.bosonnlp.com/keywords.html
Parameters
----------
text (string): text passage from which to extract keywords
top_k (integer): number of keywords to return
Returns
-------
list of key-value pairs {word: weight}
>>> nlp = BosonNLPWrapper('')
>>> nlp.extract_keywords('我最愛老虎堂，奶茶香醇，波霸彈Q 好香的黑糖味')
[{'波霸彈': 0.5980681967308248}, {'黑糖': 0.4699792421671365}, {'香醇': 0.4497614275300947}]
"""
result = self.nlp.extract_keywords(text, top_k) # outputs in sorted order of weight
return [{result[i][1]: result[i][0]} for i in range(len(result))]
def segment_words_and_tag(self, text):
"""
Splits up text into segments of "words" and tags them with their respective part of speech.
See: http://docs.bosonnlp.com/tag.html
Parameters
----------
text (string): text passage to segment into separate "words" and tags them with parts of speech
Returns
-------
list of key-value pairs {word: part-of-speech-tag}
"""
result = self.nlp.tag(text)[0]
words = result['word']
tags = result['tag']
return [{words[i]: tags[i]} for i in range(len(words))]
def get_summary(self, content, title='', pct_limit=0.2):
"""
Extracts a new digest (summary) of the content.
See: http://docs.bosonnlp.com/summary.html
Parameters
----------
text (string): text passage to summarize
title (string): title of the passage (optional, may provide more accurate results)
pct_limit (float): max length of the summary in terms of percentage of the original word count
Returns
-------
string containing the summary of the passage
"""
summary = self.nlp.summary(title, content, pct_limit)
return summary