粗浅地突击学习了一点点自然语言处理，在文章里会分析python实现的文本情感分析代码，
代码功能：分析一句话(中文)里蕴含的正面情绪和负面情绪并评分，最后返回正负面情绪的总分，平均分，标准差
写在前面：
其实实现的思路很清晰：分词 - 词语分析(正面词/负面词/程度词/否定词) - 按打分规则评分
|| 导入jieba库和numpy矩阵
import jieba
import numpy as np
|| 打开词典文件，返回列表
def open_dict(Dict = 'hahah', path=r'/Users/86133/乱七八糟的代码们/python/Textming/'):
path = path + '%s.txt' % Dict
dictionary = open(path, 'r', encoding='utf-8')
dict = []
for word in dictionary:
word = word.strip('\n')
dict.append(word)
return dict
|| 将自己的中文语料文本导入（这里你要修改path路径）。
deny_word = open_dict(Dict = '否定词', path= r'/Users/86133/乱七八糟的代码们/python/Textming/')
posdict = open_dict(Dict = 'positive', path= r'/Users/86133/乱七八糟的代码们/python/Textming/')
negdict = open_dict(Dict = 'negative', path= r'/Users/86133/乱七八糟的代码们/python/Textming/')
degree_word = open_dict(Dict = '程度级别词语', path= r'/Users/86133/乱七八糟的代码们/python/Textming/'
|| 针对程度词细分程度：extreme，very，more，ish，last （tips：用以划分的标志词需要在程度词文本中写入）
mostdict = degree_word[degree_word.index('extreme')+1 : degree_word.index('very')]#权重4，即在情感词前乘以4
verydict = degree_word[degree_word.index('very')+1 : degree_word.index('more')]#权重3
moredict = degree_word[degree_word.index('more')+1 : degree_word.index('ish')]#权重2
ishdict = degree_word[degree_word.index('ish')+1 : degree_word.index('last')]#权重0.5
|| 核心代码：评分逻辑
def judgeodd(num):
if (num % 2) == 0:
return 'even'
else:
return 'odd'
def sentiment_score_list(dataset):
seg_sentence = dataset.split('％％％')
count1 = []
count2 = []
for sen in seg_sentence: #循环遍历每一个评论
segtmp = jieba.lcut(sen, cut_all=False) #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 *= 0.5
elif w in deny_word:
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 *= 0.5
elif w in degree_word:
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 == '！' or word == '!': ##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
|| 数据汇总
为每句话创建一个矩阵，矩阵中每行代表一句话按规则划分出的一系列词组，每列代表这些词组的各项打分，
对这些矩阵中的数据进行列求和操作( 即针对一句话的每一个部分的评分进行汇总），最后结果使用一维数组返回
若将需要计算的数据放入二维数组中，将出现TypeError: list indices must be integers or slices, not tuple的报错因为在python中列表中的每一个元素大小可能不同，因此不能直接取其某一列进行操作应该利用numpy.array函数将其转变为标准矩阵，再对其进行取某一列的操作
def sentiment_score(senti_score_list):
score = []
i = 0
for review in senti_score_list:
i = i + 1
score_array = np.array(review)
Pos = np.sum(score_array[:, 0])
Neg = np.sum(score_array[:, 1])
AvgPos = np.mean(score_array[:, 0])
AvgPos = float('%.1f'%AvgPos)
AvgNeg = np.mean(score_array[:, 1])
AvgNeg = float('%.1f'%AvgNeg)
StdPos = np.std(score_array[:, 0])
StdPos = float('%.1f'%StdPos)
StdNeg = np.std(score_array[:, 1])
StdNeg = float('%.1f'%StdNeg)
score.append([Pos, Neg, AvgPos, AvgNeg, StdPos, StdNeg])
return score
|| 测试代码
data = """我是真的菜！真的垃圾！"""
data2 = """你是真的强！真的厉害！"""
arr = sentiment_score(sentiment_score_list(data))
for x in range(len(arr)):
print(arr[x])
