Python 文本挖掘：使用情感词典进行情感分析（算法及程序设计）

#! /usr/bin/env python2.7
#coding=utf-8

import pickle
import textprocessing as tp
import numpy as np

posdict = pickle.load(open('D:/code/sentiment_test/sentiment_dictionary/posdict.pkl', 'r'))
negdict = pickle.load(open('D:/code/sentiment_test/sentiment_dictionary/negdict.pkl', 'r'))
mostdict = pickle.load(open('D:/code/sentiment_test/sentiment_dictionary/mostdict.pkl', 'r'))
verydict = pickle.load(open('D:/code/sentiment_test/sentiment_dictionary/verydict.pkl', 'r'))
moredict = pickle.load(open('D:/code/sentiment_test/sentiment_dictionary/moredict.pkl', 'r'))
ishdict = pickle.load(open('D:/code/sentiment_test/sentiment_dictionary/ishdict.pkl', 'r'))
insufficientdict = pickle.load(open('D:/code/sentiment_test/sentiment_dictionary/insufficentdict.pkl', 'r'))
inversedict = pickle.load(open('D:/code/sentiment_test/sentiment_dictionary/inversedict.pkl', 'r'))

review = pickle.load(open('D:/code/review_set/review_pkl/Motorala.pkl', 'r'))

def judgeodd(num):
if (num/2)*2 == num:
return 'even'
else:
return 'odd'

def sentiment_score_list(dataset):
cuted_data = []
for cell in dataset:
cuted_data.append(tp.cut_sentence(cell))

count1 = []
count2 = []
for sents in cuted_data: #循环遍历每一个评论
for sent in sents: #循环遍历评论中的每一个分句
segtmp = tp.segmentation(sent, 'list') #把句子进行分词，以列表的形式返回
i = 0 #记录扫描到的词的位置
a = 0 #记录情感词的位置
poscount = 0 #积极词的第一次分值
poscount2 = 0 #积极词反转后的分值
poscount3 = 0 #积极词的最后分值（包括叹号的分值）
negcount = 0
negcount2 = 0
negcount3 = 0
for word in segtmp:
if word in posdict: #判断词语是否是情感词
poscount += 1
c = 0
for w in segtmp[a:i]: #扫描情感词前的程度词
if w in mostdict:
poscount *= 4.0
elif w in verydict:
poscount *= 3.0
elif w in moredict:
poscount *= 2.0
elif w in ishdict:
poscount /= 2.0
elif w in insufficientdict:
poscount /= 4.0
elif w in inversedict:
c += 1
if judgeodd(c) == 'odd': #扫描情感词前的否定词数
poscount *= -1.0
poscount2 += poscount
poscount = 0
poscount3 = poscount + poscount2 + poscount3
poscount2 = 0
else:
poscount3 = poscount + poscount2 + poscount3
poscount = 0
a = i + 1 #情感词的位置变化
elif word in negdict: #消极情感的分析，与上面一致
negcount += 1
d = 0
for w in segtmp[a:i]:
if w in mostdict:
negcount *= 4.0
elif w in verydict:
negcount *= 3.0
elif w in moredict:
negcount *= 2.0
elif w in ishdict:
negcount /= 2.0
elif w in insufficientdict:
negcount /= 4.0
elif w in inversedict:
d += 1
if judgeodd(d) == 'odd':
negcount *= -1.0
negcount2 += negcount
negcount = 0
negcount3 = negcount + negcount2 + negcount3
negcount2 = 0
else:
negcount3 = negcount + negcount2 + negcount3
negcount = 0
a = i + 1
elif word == '！'.decode('utf8') or word == '!'.decode('utf8'): ##判断句子是否有感叹号
for w2 in segtmp[::-1]: #扫描感叹号前的情感词，发现后权值+2，然后退出循环
if w2 in posdict or negdict:
poscount3 += 2
negcount3 += 2
break
i += 1 #扫描词位置前移

#以下是防止出现负数的情况
pos_count = 0
neg_count = 0
if poscount3 < 0 and negcount3 > 0:
neg_count += negcount3 - poscount3
pos_count = 0
elif negcount3 < 0 and poscount3 > 0:
pos_count = poscount3 - negcount3
neg_count = 0
elif poscount3 < 0 and negcount3 < 0:
neg_count = -poscount3
pos_count = -negcount3
else:
pos_count = poscount3
neg_count = negcount3

count1.append([pos_count, neg_count])
count2.append(count1)
count1 = []

return count2

def sentiment_score(senti_score_list):
score = []
for review in senti_score_list:
score_array = np.array(review)
Pos = np.sum(score_array[:,0])
Neg = np.sum(score_array[:,1])
AvgPos = np.mean(score_array[:,0])
AvgNeg = np.mean(score_array[:,1])
StdPos = np.std(score_array[:,0])
StdNeg = np.std(score_array[:,1])
score.append([Pos, Neg, AvgPos, AvgNeg, StdPos, StdNeg])
return score