def query_meituan_hs():
# offset = request.values.get('offset')
# pagesize = request.values.get('pageSize')
# dir(SnowNLP)
# resp = modles.HsMeituanModel.query.offset(offset).limit(pagesize)
# total = modles.HsMeituanModel.query.count()
# # 评论总数
# # count = XymTripModel.query.count()
# # 进行情感分析
# # cut = jieba.cut(comment, cut_all=False)
# # print("Full Mode: " + "/ ".join(cut)) # 全模式
# lst = []
# print(dir(resp))
# for page in resp:
# row = ""
# # keywords = analyse.textrank(page.comment)
# # for k in keywords:
# # print(f'{k}//')
# # seg = jieba.lcut(page.comment)
# # print(seg)
# s = SnowNLP(page.comment)
# # 把一条评论进行分句
# for word in s.sentences:
# # 分句之后每段词的情感度
# single = SnowNLP(word)
# if single.sentiments <= 0.15:
# row += f"<span style='color:red'>{word}</span>"
# else:
# row += word
# finally_row = f"<p>{row}</p>"
# lst.append(finally_row)
data = comment_process(modles.HsMeituanModel.query)
return custom_response.SuccessResponse(data=data[0], total=data[1])
def query_qunar_hs():
data = comment_process(modles.HsQunarModel.query)
return custom_response.SuccessResponse(data=data[0], total=data[1])
def query_meituan_xym():
data = comment_process(modles.XymMeituanModel.query)
return custom_response.SuccessResponse(data=data[0], total=data[1])
def analysis_xym(year):
resp = analysis(XYMModel, year)
return custom_response.SuccessResponse(data=resp)
def analysis_hs(year):
resp = analysis(HuaShanModel, year)
return custom_response.SuccessResponse(data=resp)
def resp_xym():
resp = resp_process(XYMModel)
return custom_response.SuccessResponse(data=resp[0], total=resp[1])
def resp_hs():
resp = resp_process(HuaShanModel)
return custom_response.SuccessResponse(data=resp[0], total=resp[1])
def query_xiec_xym():
data = comment_process(modles.XymXieChengModel.query)
return custom_response.SuccessResponse(data=data[0], total=data[1])
def query_trip_xym():
data = comment_process(modles.XymTripModel.query)
return custom_response.SuccessResponse(data=data[0], total=data[1])