def getTypeInt(text):
formatInput(text)
tms.tms_segment("./twitterData/twitterTestData.txt",[1],"./twitterData/twitterTestData1.txt","^","\t",1)
#类型预测
tms.tms_predict("./twitterData/twitterTestData1.txt","./twitterData/type_model/model/tms.config",result_save_path="./twitterData/tms.result")
file = open("twitterData/tms.result")
text = file.read()
text = text.split("\n")
#情感预测
tms.tms_predict("./twitterData/twitterTestData1.txt","./twitterData/emotion/model/tms.config",result_save_path="./twitterData/emotion/tms.result")
file1 = open("twitterData/emotion/tms.result")
text1 = file1.read()
text1 = text1.split("\n")
result = []
for i in range(0,len(text)):
if len(text[i]) > 0:
each = text[i].split("\t")
point = int(float(each[0]))
each1 = text1[i].split("\t")
point1 = int(float(each1[0]))
result.append([point,point1])
return result
def getTypeStr(text):
dir = ["新闻，娱乐类","自然灾难","航空事件","食品药物安全","饮食","科教，教育","国际事件","招聘","犯罪事件","交通事故","经济金融类","环境问题","其他","煤矿事件","医疗，疾病","科技"]
dir1 = ["积极","消极","中立"]
formatInput(text)
tms.tms_segment("./twitterData/twitterTestData.txt",[1],"./twitterData/twitterTestData1.txt","^","\t",1)
#类型预测
tms.tms_predict("./twitterData/twitterTestData1.txt","./twitterData/type_model/model/tms.config",result_save_path="./twitterData/tms.result")
file = open("twitterData/tms.result")
text = file.read()
text = text.split("\n")
#情感预测
tms.tms_predict("./twitterData/twitterTestData1.txt","./twitterData/emotion/model/tms.config",result_save_path="./twitterData/emotion/tms.result")
file1 = open("twitterData/emotion/tms.result")
text1 = file1.read()
text1 = text1.split("\n")
result = []
for i in range(0,len(text)):
if len(text[i]) > 0:
each = text[i].split("\t")
point = int(float(each[0]))
each1 = text1[i].split("\t")
point1 = int(float(each1[0]))
result.append([dir[point].decode("utf-8"),dir1[point1].decode("utf-8")])
return result
def getTypeInt(text):
formatInput(text)
tms.tms_segment("./twitterData/twitterTestData.txt",[1],"./twitterData/twitterTestData1.txt","^","\t",1)
#类型预测
tms.tms_predict("./twitterData/twitterTestData1.txt","./twitterData/type_model/model/tms.config",result_save_path="./twitterData/tms.result")
file = open("twitterData/tms.result")
text = file.read()
text = text.split("\n")
#情感预测
tms.tms_predict("./twitterData/twitterTestData1.txt","./twitterData/emotion/model/tms.config",result_save_path="./twitterData/emotion/tms.result")
file1 = open("twitterData/emotion/tms.result")
text1 = file1.read()
text1 = text1.split("\n")
result = []
for i in range(0,len(text)):
if len(text[i]) > 0:
each = text[i].split("\t")
point = int(float(each[0]))
each1 = text1[i].split("\t")
point1 = int(float(each1[0]))
result.append([point,point1])
return result
def getTypeStr(text):
dir = ["新闻，娱乐类","自然灾难","航空事件","食品药物安全","饮食","科教，教育","国际事件","招聘","犯罪事件","交通事故","经济金融类","环境问题","其他","煤矿事件","医疗，疾病","科技"]
dir1 = ["积极","消极","中立"]
formatInput(text)
tms.tms_segment("./twitterData/twitterTestData.txt",[1],"./twitterData/twitterTestData1.txt","^","\t",1)
#类型预测
tms.tms_predict("./twitterData/twitterTestData1.txt","./twitterData/type_model/model/tms.config",result_save_path="./twitterData/tms.result")
file = open("twitterData/tms.result")
text = file.read()
text = text.split("\n")
#情感预测
tms.tms_predict("./twitterData/twitterTestData1.txt","./twitterData/emotion/model/tms.config",result_save_path="./twitterData/emotion/tms.result")
file1 = open("twitterData/emotion/tms.result")
text1 = file1.read()
text1 = text1.split("\n")
result = []
for i in range(0,len(text)):
if len(text[i]) > 0:
each = text[i].split("\t")
point = int(float(each[0]))
each1 = text1[i].split("\t")
point1 = int(float(each1[0]))
result.append([dir[point].decode("utf-8"),dir1[point1].decode("utf-8")])
return result
def getEmotionType(text):
formatInput(text)
curpath = os.path.join(os.path.dirname(__file__), 'twitterData/').replace('\\','/')
#tms.tms_segment("./EMOTION/twitterData/twitterTestData.txt",[1],"./EMOTION/twitterData/twitterTestData1.txt","^","\t",1)
tms.tms_segment(curpath+"twitterTestData.txt",[1],curpath+"twitterTestData1.txt","^","\t",1)
#情感预测,0积极，1消极，2中立
tms.tms_predict(curpath+"twitterTestData1.txt",curpath+"emotion/model/tms.config",result_save_path=curpath+"emotion/tms.result")
file1 = open(curpath+"emotion/tms.result")
text1 = file1.read()
text1 = text1.split("\n")
result = []
for each in text1:
each = each.split('\t')
result.append(each[0])
return result
def getEmotionType(text):
formatInput(text)
curpath = os.path.join(os.path.dirname(__file__),
'twitterData/').replace('\\', '/')
#tms.tms_segment("./EMOTION/twitterData/twitterTestData.txt",[1],"./EMOTION/twitterData/twitterTestData1.txt","^","\t",1)
tms.tms_segment(curpath + "twitterTestData.txt", [1],
curpath + "twitterTestData1.txt", "^", "\t", 1)
#情感预测,0积极，1消极，2中立
tms.tms_predict(curpath + "twitterTestData1.txt",
curpath + "emotion/model/tms.config",
result_save_path=curpath + "emotion/tms.result")
file1 = open(curpath + "emotion/tms.result")
text1 = file1.read()
text1 = text1.split("\n")
result = []
for each in text1:
each = each.split('\t')
result.append(each[0])
return result
