def quickAnalyze():
# dbからデータを読み込む
u = LoadUserData(conf_path)
dbSession = model.startSession(u)
table, footer = readReplyTable.read(exec_path + "/common/replyTable.json")
regexes = makeRegexes(table)
# 前回の更新時間から現在までのデータを入手する
q = dbSession.query(model.Tweet)
tq = q.filter(model.Tweet.isAnalyze == 0)[:10000]
for t in tq:
#1発言毎
t.text = RemoveCharacter(t.text)
#print_d2(t.text)
analyzeReply2(t,dbSession,table,regexes)
t.isAnalyze = 1
t_enc = t.text.encode(g_mecabencode,'ignore')
sarray = mecab.sparse_all(t_enc,mecabPath).split("\n")
sarray2 = connectUnderScore(sarray)
markovWordList,topNWordList = TakeWordList(sarray)
#最近出た名詞貯める
for tn in topNWordList:
hot = model.Hot()
hot.word = unicode(tn,g_systemencode)
dbSession.add(hot)
dbSession.add(t)
dbSession.commit()
def sparse_sentence(self, s):
#print s
s_sparse =\
mecab.sparse_all(s.encode("utf-8"),mecab_path).split("\n")[:-2]
candidate = set()
for s2 in s_sparse: # この時点で単語レベルのハズ(ただしs2=単語 品詞
# とかかなぁ
#print "s2",
s3 = s2.decode("utf-8").split("\t")
s4 = s3[1].split(",")
#print s3[0],s4[0]
if s4[0] != u"記号" and s4[0] != u"助動詞" \
and s4[0] != u"助詞":#数が集まったら名詞のみにしたい
candidate.add(s3[0])
return candidate
def sparse_sentence(self, s):
#print s
s_sparse =\
mecab.sparse_all(s.encode("utf-8"),mecab_path).split("\n")[:-2]
candidate = set()
for s2 in s_sparse: # この時点で単語レベルのハズ(ただしs2=単語 品詞
# とかかなぁ
#print "s2",
s3 = s2.decode("utf-8").split("\t")
s4 = s3[1].split(",")
#print s3[0],s4[0]
if s4[0] != u"記号" and s4[0] != u"助動詞" \
and s4[0] != u"助詞":#数が集まったら名詞のみにしたい
candidate.add(s3[0])
return candidate
def sparse_sentence(s):
#print s
s_sparse =\
mecab.sparse_all(s.encode("utf-8"),"/usr/lib/libmecab.so.1").split("\n")[:-2]
candidate = set()
for s2 in s_sparse: # この時点で単語レベルのハズ(ただしs2=単語 品詞
# とかかなぁ
#print "s2",
s3 = s2.decode("utf-8").split("\t")
s4 = s3[1].split(",")
if s4[0] == u"名詞":
#if s4[0] != u"記号" and s4[0] != u"助動詞" \
# and s4[0] != u"助詞":#数が集まったら名詞のみにしたい
# print s3[0],s4[0],s4[1]
if not stopwords(s3[0]):
candidate.add(s3[0])
return candidate