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emotion_eng.py
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# -*- coding: utf-8 -*-
# @Time : 18-9-18 上午9:23
# @Author : Redtree
# @File : emotion_eng.py
# @Desc : 文本情感分析算法 （英文）
import load_dict
all_list = load_dict.getAllList()
import nltk
import random
#文本情感分析算法
def cutSentence(input): #结巴分词
res = nltk.word_tokenize(input) # 默认是精确模式
return res
def reduceFunc(reduceTimes): #适用于累加条件下的衰减法 （pos and neg）
addY=(10/reduceTimes)/10
return addY #衰减后当此追加的情感值
def levelReduceFunc(levelReduceTimes,level,type): #适用于区间叠乘条件下的衰减法 （level）
if level==6:
levela=2 #区间下限
levelb=2.5 #区间上限
levelER=0.67 #衰减系数
if level==5:
levela=1.6 #区间下限
levelb=1.9 #区间上限
levelER=0.67 #衰减系数
if level==4:
levela=1.2 #区间下限
levelb=1.6 #区间上限
levelER=0.67 #衰减系数
if level==3:
levela=0.6 #区间下限
levelb=0.9 #区间上限
levelER=1.23 #衰减系数
if level == 2:
levela = 0.4 # 区间下限
levelb = 0.7 # 区间上限
levelER = 1.23 # 衰减系数
if level == 1:
levela = 0.2 # 区间下限
levelb = 0.5 # 区间上限
levelER = 1.23 # 衰减系数
if level >= 4:
if type == 'topLimit': #上限衰减
levelbCheck = levelb * (levelER ** (levelReduceTimes - 1))
if levelbCheck<=1.001:
levelbCheck = 1.001
return levelbCheck
if type == 'lowerLimit': #下限衰减
levelaCheck = levela * (levelER ** (levelReduceTimes - 1))
if levelaCheck<=1.000:
levelaCheck = 1.000
return levelaCheck
if level <=3 :
if type == 'topLimit': # 上限衰减
levelbCheck = levelb * (levelER ** (levelReduceTimes - 1))
if levelbCheck >= 1.000:
levelbCheck = 1.000
return levelbCheck
if type == 'lowerLimit': # 下限衰减
levelaCheck = levela * (levelER ** (levelReduceTimes - 1))
if levelaCheck >= 0.998:
levelaCheck = 0.998
return levelaCheck
def checkMoodValue(segWord): #获取句子的情感能量值 mv =(posV*(functionE)+negV*(functionE))*isFouDing*chekLevel
MoodValue = 0 #functionE 为待加算法，用于处理重复数据的量级衰减
isFouDing = 1
checkLevel=1
posreduceTimes=1
negreduceTimes=1
level6ReduceTimes=1
level5ReduceTimes=1
level4ReduceTimes=1
level3ReduceTimes = 1
level2ReduceTimes = 1
level1ReduceTimes = 1
# 正能量词Check
for onePos in all_list.positive_words_eng:
if str(onePos).__contains__(' '):
if str(onePos) in str(segWord):
MoodValue = MoodValue + reduceFunc(posreduceTimes);
posreduceTimes = posreduceTimes + 1;
break
else:
if str(onePos) in cutSentence(str(segWord)):
MoodValue = MoodValue + reduceFunc(posreduceTimes);
posreduceTimes = posreduceTimes + 1;
break
# 负能量词Check
for oneNeg in all_list.negative_words_eng:
if str(oneNeg).__contains__(' '):
if str(oneNeg) in str(segWord):
MoodValue = MoodValue - reduceFunc(negreduceTimes);
negreduceTimes = negreduceTimes + 1;
break
else:
if str(oneNeg) in cutSentence(str(segWord)):
MoodValue = MoodValue - reduceFunc(negreduceTimes);
negreduceTimes = negreduceTimes + 1;
break
# 否定词Check
for fdword in all_list.fouding_words_eng:
if str(fdword).__contains__(' '):
if str(fdword) in str(segWord):
isFouDing = isFouDing * (-1)
break
else:
if str(fdword) in cutSentence(str(segWord)):
isFouDing = isFouDing * (-1)
break
# 程度级Check 矫正系数 er
# level1 矫正系数(0.2~0.5)
# level2 矫正系数(0.4~0.7)
# level3 矫正系数(0.6~0.9)
# level4 矫正系数(1.2~1.6)
# level5 矫正系数(1.6~1.9)
# level6 矫正系数(2.0~2.5)
for oneLevel in all_list.level1_words_eng:
if str(oneLevel).__contains__(' '):
if str(oneLevel) in str(segWord):
er = random.uniform(levelReduceFunc(level1ReduceTimes, 1, 'lowerLimit'),
levelReduceFunc(level1ReduceTimes, 1, 'topLimit'))
checkLevel = checkLevel * er
break
else:
if str(oneLevel) in cutSentence(str(segWord)):
er = random.uniform(levelReduceFunc(level1ReduceTimes, 1, 'lowerLimit'),
levelReduceFunc(level1ReduceTimes, 1, 'topLimit'))
checkLevel = checkLevel * er
break
for oneLevel in all_list.level2_words_eng:
if str(oneLevel).__contains__(' '):
if str(oneLevel) in str(segWord):
er = random.uniform(levelReduceFunc(level2ReduceTimes, 2, 'lowerLimit'),
levelReduceFunc(level2ReduceTimes, 2, 'topLimit'))
checkLevel = checkLevel * er
break
else:
if str(oneLevel) in cutSentence(str(segWord)):
er = random.uniform(levelReduceFunc(level2ReduceTimes, 2, 'lowerLimit'),
levelReduceFunc(level2ReduceTimes, 2, 'topLimit'))
checkLevel = checkLevel * er
break
for oneLevel in all_list.level3_words_eng:
if str(oneLevel).__contains__(' '):
if str(oneLevel) in str(segWord):
er = random.uniform(levelReduceFunc(level3ReduceTimes, 3, 'lowerLimit'),
levelReduceFunc(level3ReduceTimes, 3, 'topLimit'))
checkLevel = checkLevel * er
break
else:
if str(oneLevel) in cutSentence(str(segWord)):
er = random.uniform(levelReduceFunc(level3ReduceTimes, 3, 'lowerLimit'),
levelReduceFunc(level3ReduceTimes, 3, 'topLimit'))
checkLevel = checkLevel * er
break
for oneLevel in all_list.level4_words_eng:
if str(oneLevel).__contains__(' '):
if str(oneLevel) in str(segWord):
er = random.uniform(levelReduceFunc(level4ReduceTimes, 4, 'lowerLimit'),
levelReduceFunc(level4ReduceTimes, 4, 'topLimit'))
checkLevel = checkLevel * er
break
else:
if str(oneLevel) in cutSentence(str(segWord)):
er = random.uniform(levelReduceFunc(level4ReduceTimes, 4, 'lowerLimit'),
levelReduceFunc(level4ReduceTimes, 4, 'topLimit'))
checkLevel = checkLevel * er
break
for oneLevel in all_list.level5_words_eng:
if str(oneLevel).__contains__(' '):
if str(oneLevel) in str(segWord):
er = random.uniform(levelReduceFunc(level5ReduceTimes, 5, 'lowerLimit'),
levelReduceFunc(level5ReduceTimes, 5, 'topLimit'))
checkLevel = checkLevel * er
break
else:
if str(oneLevel) in cutSentence(str(segWord)):
er = random.uniform(levelReduceFunc(level5ReduceTimes, 5, 'lowerLimit'),
levelReduceFunc(level5ReduceTimes, 5, 'topLimit'))
checkLevel = checkLevel * er
break
for oneLevel in all_list.level6_words_eng:
if str(oneLevel).__contains__(' '):
if str(oneLevel) in str(segWord):
er = random.uniform(levelReduceFunc(level6ReduceTimes, 6, 'lowerLimit'),
levelReduceFunc(level6ReduceTimes, 6, 'topLimit'))
checkLevel = checkLevel * er
break
else:
if str(oneLevel) in cutSentence(str(segWord)):
er = random.uniform(levelReduceFunc(level6ReduceTimes, 6, 'lowerLimit'),
levelReduceFunc(level6ReduceTimes, 6, 'topLimit'))
checkLevel = checkLevel * er
break
MoodValue = MoodValue * isFouDing * checkLevel
return MoodValue
def getMoodValue(text):
try:
text = str(text).replace('，', '，|')
text = str(text).replace('。', '。|')
text = str(text).replace(',', '，|')
text = str(text).replace('.', '。|')
text = str(text).replace('!', '!|')
text = str(text).replace('！', '！|')
text = str(text).replace('？', '？|')
text = str(text).replace('?', '?|')
tsp_list = text.split('|')
all_mv = 0
re_obj = []
for tl in tsp_list:
if not (tl == '' or tl == ' ' ):
tmp_MoodValue = checkMoodValue(tl)
all_mv = all_mv + tmp_MoodValue
cobj = {'text':tl,'value':round(tmp_MoodValue,6)}
re_obj.append(cobj)
res = {'all_value':round(all_mv,6),'split':re_obj}
return res
except Exception as err:
print('文本情感分析失败'+str(err))
return 'error'