基于情感词典的情感打分
原理我就不讲了，请移步下面这篇论文，包括情感词典的构建（各位读者可以根据自己的需求稍作简化），以及打分策略（程序对原论文稍有改动）。
论文在这里下载：基于情感词典的中文微博情感倾向性研究-陈晓东-华中科技大学
(大家可以上百度学术搜索下载)
本文采用的方法如下：
首先对单条微博进行文本预处理，并以标点符号为分割标志，将单条微博分割为n个句子，提取每个句子中的情感词 。以下两步的处理均以分句为处理单位。
第二步在情感词表中寻找情感词，以每个情感词为基准，向前依次寻找程度副词、否定词，并作相应分值计算。随后对分句中每个情感词的得分作求和运算。
第三步判断该句是否为感叹句，是否为反问句，以及是否存在表情符号。如果是，则分句在原有分值的基础上加上或减去对应的权值。
最后对该条微博的所有分句的分值进行累加，获得该条微博的最终得分。
代码如下：
首先文件结构图如下：
其中，degree_dict为程度词典，其中每个文件为不同的权值。
emotion_dict为情感词典，包括了积极情感词和消极情感词以及停用词。
文件一：文本预处理 textprocess.py
在里面封装了一些文本预处理的函数，方便调用。
# -*- coding: utf-8 -*-
__author__ = 'Bai Chenjia'
import jieba
import jieba.posseg as pseg
print "加载用户词典..."
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
jieba.load_userdict('C://Python27/Lib/site-packages/jieba/user_dict/pos_dict.txt')
jieba.load_userdict('C://Python27/Lib/site-packages/jieba/user_dict/neg_dict.txt')
# 分词，返回List
def segmentation(sentence):
seg_list = jieba.cut(sentence)
seg_result = []
for w in seg_list:
seg_result.append(w)
#print seg_result[:]
return seg_result
# 分词，词性标注，词和词性构成一个元组
def postagger(sentence):
pos_data = pseg.cut(sentence)
pos_list = []
for w in pos_data:
pos_list.append((w.word, w.flag))
#print pos_list[:]
return pos_list
# 句子切分
def cut_sentence(words):
words = words.decode('utf8')
start = 0
i = 0
token = 'meaningless'
sents = []
punt_list = ',.!?;~，。！？；～… '.decode('utf8')
#print "punc_list", punt_list
for word in words:
#print "word", word
if word not in punt_list: # 如果不是标点符号
#print "word1", word
i += 1
token = list(words[start:i+2]).pop()
#print "token:", token
elif word in punt_list and token in punt_list: # 处理省略号
#print "word2", word
i += 1
token = list(words[start:i+2]).pop()
#print "token:", token
else:
#print "word3", word
sents.append(words[start:i+1]) # 断句
start = i + 1
i += 1
if start < len(words): # 处理最后的部分
sents.append(words[start:])
return sents
def read_lines(filename):
fp = open(filename, 'r')
lines = []
for line in fp.readlines():
line = line.strip()
line = line.decode("utf-8")
lines.append(line)
fp.close()
return lines
# 去除停用词
def del_stopwords(seg_sent):
stopwords = read_lines("f://Sentiment_dict/emotion_dict/stop_words.txt") # 读取停用词表
new_sent = [] # 去除停用词后的句子
for word in seg_sent:
if word in stopwords:
continue
else:
new_sent.append(word)
return new_sent
# 获取六种权值的词，根据要求返回list，这个函数是为了配合Django的views下的函数使用
def read_quanzhi(request):
result_dict = []
if request == "one":
result_dict = read_lines("f://emotion/mysite/Sentiment_dict/degree_dict/most.txt")
elif request == "two":
result_dict = read_lines("f://emotion/mysite/Sentiment_dict/degree_dict/very.txt")
elif request == "three":
result_dict = read_lines("f://emotion/mysite/Sentiment_dict/degree_dict/more.txt")
elif request == "four":
result_dict = read_lines("f://emotion/mysite/Sentiment_dict/degree_dict/ish.txt")
elif request == "five":
result_dict = read_lines("f://emotion/mysite/Sentiment_dict/degree_dict/insufficiently.txt")
elif request == "six":
result_dict = read_lines("f://emotion/mysite/Sentiment_dict/degree_dict/inverse.txt")
else:
pass
return result_dict
if __name__ == '__main__':
test_sentence1 = "这款手机大小合适。"
test_sentence2 = "这款手机大小合适，配置也还可以，很好用，只是屏幕有点小。。。总之，戴妃+是一款值得购买的智能手机。"
test_sentence3 = "这手机的画面挺好，操作也比较流畅。不过拍照真的太烂了！系统也不好。"
"""
seg_result = segmentation(test_sentence3) # 分词，输入一个句子，返回一个list
for w in seg_result:
print w,
print '\n'
"""
"""
new_seg_result = del_stopwords(seg_result) # 去除停用词
for w in new_seg_result:
print w,
"""
#postagger(test_sentence1) # 分词，词性标注，词和词性构成一个元组
#cut_sentence(test_sentence2) # 句子切分
#lines = read_lines("f://Sentiment_dict/emotion_dict/posdict.txt")
#print lines[:]文件二：情感打分 dict_main.py
其中待处理数据放在chinese_weibo.txt中，读者可以自行更改文件目录，该文件中的数据格式如下图：
即用每一行代表一条语句，我们对每条语句进行情感分析，进行打分
# -*- coding: utf-8 -*-
__author__ = 'Bai Chenjia'
import text_process as tp
import numpy as np
# 1.读取情感词典和待处理文件
# 情感词典
print "reading..."
posdict = tp.read_lines("f://emotion/mysite/Sentiment_dict/emotion_dict/pos_all_dict.txt")
negdict = tp.read_lines("f://emotion/mysite/Sentiment_dict/emotion_dict/neg_all_dict.txt")
# 程度副词词典
mostdict = tp.read_lines('f://emotion/mysite/Sentiment_dict/degree_dict/most.txt') # 权值为2
verydict = tp.read_lines('f://emotion/mysite/Sentiment_dict/degree_dict/very.txt') # 权值为1.5
moredict = tp.read_lines('f://emotion/mysite/Sentiment_dict/degree_dict/more.txt') # 权值为1.25
ishdict = tp.read_lines('f://emotion/mysite/Sentiment_dict/degree_dict/ish.txt') # 权值为0.5
insufficientdict = tp.read_lines('f://emotion/mysite/Sentiment_dict/degree_dict/insufficiently.txt') # 权值为0.25
inversedict = tp.read_lines('f://emotion/mysite/Sentiment_dict/degree_dict/inverse.txt') # 权值为-1
# 情感级别
emotion_level1 = "悲伤。在这个级别的人过的是八辈子都懊丧和消沉的生活。这种生活充满了对过去的懊悔、自责和悲恸。在悲伤中的人，看这个世界都是灰黑色的。"
emotion_level2 = "愤怒。如果有人能跳出冷漠和内疚的怪圈，并摆脱恐惧的控制，他就开始有欲望了，而欲望则带来挫折感，接着引发愤怒。愤怒常常表现为怨恨和复仇心里，它是易变且危险的。愤怒来自未能满足的欲望，来自比之更低的能量级。挫败感来自于放大了欲望的重要性。愤怒很容易就导致憎恨，这会逐渐侵蚀一个人的心灵。"
emotion_level3 = "淡定。到达这个能级的能量都变得很活跃了。淡定的能级则是灵活和无分别性的看待现实中的问题。到来这个能级，意味着对结果的超然，一个人不会再经验挫败和恐惧。这是一个有安全感的能级。到来这个能级的人们，都是很容易与之相处的，而且让人感到温馨可靠,这样的人总是镇定从容。他们不会去强迫别人做什么。"
emotion_level4 = "平和。他感觉到所有的一切都生机勃勃并光芒四射，虽然在其他人眼里这个世界还是老样子，但是在这人眼里世界却是一个。所以头脑保持长久的沉默，不再分析判断。观察者和被观察者成为同一个人，观照者消融在观照中，成为观照本身。"
emotion_level5 = "喜悦。当爱变得越来越无限的时候，它开始发展成为内在的喜悦。这是在每一个当下，从内在而非外在升起的喜悦。这个能级的人的特点是，他们具有巨大的耐性，以及对一再显现的困境具有持久的乐观态度，以及慈悲。同时发生着。在他们开来是稀松平常的作为，却会被平常人当成是奇迹来看待。"
# 情感波动级别
emotion_level6 = "情感波动很小，个人情感是不易改变的、经得起考验的。能够理性的看待周围的人和事。"
emotion_level7 = "情感波动较大，周围的喜悦或者悲伤都能轻易的感染他，他对周围的事物有敏感的认知。"
# 2.程度副词处理，根据程度副词的种类不同乘以不同的权值
def match(word, sentiment_value):
if word in mostdict:
sentiment_value *= 2.0
elif word in verydict:
sentiment_value *= 1.75
elif word in moredict:
sentiment_value *= 1.5
elif word in ishdict:
sentiment_value *= 1.2
elif word in insufficientdict:
sentiment_value *= 0.5
elif word in inversedict:
#print "inversedict", word
sentiment_value *= -1
return sentiment_value
# 3.情感得分的最后处理，防止出现负数
# Example: [5, -2] → [7, 0]; [-4, 8] → [0, 12]
def transform_to_positive_num(poscount, negcount):
pos_count = 0
neg_count = 0
if poscount < 0 and negcount >= 0:
neg_count += negcount - poscount
pos_count = 0
elif negcount < 0 and poscount >= 0:
pos_count = poscount - negcount
neg_count = 0
elif poscount < 0 and negcount < 0:
neg_count = -poscount
pos_count = -negcount
else:
pos_count = poscount
neg_count = negcount
return (pos_count, neg_count)
# 求单条微博语句的情感倾向总得分
def single_review_sentiment_score(weibo_sent):
single_review_senti_score = []
cuted_review = tp.cut_sentence(weibo_sent) # 句子切分，单独对每个句子进行分析
for sent in cuted_review:
seg_sent = tp.segmentation(sent) # 分词
seg_sent = tp.del_stopwords(seg_sent)[:]
#for w in seg_sent:
# print w,
i = 0 # 记录扫描到的词的位置
s = 0 # 记录情感词的位置
poscount = 0 # 记录该分句中的积极情感得分
negcount = 0 # 记录该分句中的消极情感得分
for word in seg_sent: # 逐词分析
#print word
if word in posdict: # 如果是积极情感词
#print "posword:", word
poscount += 1 # 积极得分+1
for w in seg_sent[s:i]:
poscount = match(w, poscount)
#print "poscount:", poscount
s = i + 1 # 记录情感词的位置变化
elif word in negdict: # 如果是消极情感词
#print "negword:", word
negcount += 1
for w in seg_sent[s:i]:
negcount = match(w, negcount)
#print "negcount:", negcount
s = i + 1
# 如果是感叹号，表示已经到本句句尾
elif word == "！".decode("utf-8") or word == "!".decode('utf-8'):
for w2 in seg_sent[::-1]: # 倒序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:
poscount += 2
break
elif w2 in negdict:
negcount += 2
break
i += 1
#print "poscount,negcount", poscount, negcount
single_review_senti_score.append(transform_to_positive_num(poscount, negcount)) # 对得分做最后处理
pos_result, neg_result = 0, 0 # 分别记录积极情感总得分和消极情感总得分
for res1, res2 in single_review_senti_score: # 每个分句循环累加
pos_result += res1
neg_result += res2
#print pos_result, neg_result
result = pos_result - neg_result # 该条微博情感的最终得分
result = round(result, 1)
return result
"""
# 测试
weibo_sent = "这手机的画面挺好，操作也比较流畅。不过拍照真的太烂了！系统也不好。"
score = single_review_sentiment_score(weibo_sent)
print score
"""
# 分析test_data.txt 中的所有微博，返回一个列表，列表中元素为（分值，微博）元组
def run_score():
fp_test = open('f://emotion/mysite/Weibo_crawler/chinese_weibo.txt', 'r') # 待处理数据
contents = []
for content in fp_test.readlines():
content = content.strip()
content = content.decode("utf-8")
contents.append(content)
fp_test.close()
results = []
for content in contents:
score = single_review_sentiment_score(content) # 对每条微博调用函数求得打分
results.append((score, content)) # 形成（分数，微博）元组
return results
# 将（分值，句子）元组按行写入结果文件test_result.txt中
def write_results(results):
fp_result = open('test_result.txt', 'w')
for result in results:
fp_result.write(str(result[0]))
fp_result.write(' ')
fp_result.write(result[1])
fp_result.write('\n')
fp_result.close()
# 求取测试文件中的正负极性的微博比，正负极性分值的平均值比，正负分数分别的方差
def handel_result(results):
# 正极性微博数量，负极性微博数量，中性微博数量，正负极性比值
pos_number, neg_number, mid_number, number_ratio = 0, 0, 0, 0
# 正极性平均得分，负极性平均得分， 比值
pos_mean, neg_mean, mean_ratio = 0, 0, 0
# 正极性得分方差，负极性得分方差
pos_variance, neg_variance, var_ratio = 0, 0, 0
pos_list, neg_list, middle_list, total_list = [], [], [], []
for result in results:
total_list.append(result[0])
if result[0] > 0:
pos_list.append(result[0]) # 正极性分值列表
elif result[0] < 0:
neg_list.append(result[0]) # 负极性分值列表
else:
middle_list.append(result[0])
#################################各种极性微博数量统计
pos_number = len(pos_list)
neg_number = len(neg_list)
mid_number = len(middle_list)
total_number = pos_number + neg_number + mid_number
number_ratio = pos_number/neg_number
pos_number_ratio = round(float(pos_number)/float(total_number), 2)
neg_number_ratio = round(float(neg_number)/float(total_number), 2)
mid_number_ratio = round(float(mid_number)/float(total_number), 2)
text_pos_number = "积极微博条数为 " + str(pos_number) + " 条，占全部微博比例的 %" + str(pos_number_ratio*100)
text_neg_number = "消极微博条数为 " + str(neg_number) + " 条，占全部微博比例的 %" + str(neg_number_ratio*100)
text_mid_number = "中性情感微博条数为 " + str(mid_number) + " 条，占全部微博比例的 %" + str(mid_number_ratio*100)
##################################正负极性平均得分统计
pos_array = np.array(pos_list)
neg_array = np.array(neg_list) # 使用numpy导入，便于计算
total_array = np.array(total_list)
pos_mean = pos_array.mean()
neg_mean = neg_array.mean()
total_mean = total_array.mean() # 求单个列表的平均值
mean_ratio = pos_mean/neg_mean
if pos_mean <= 6: # 赋予不同的情感等级
text_pos_mean = emotion_level4
else:
text_pos_mean = emotion_level5
if neg_mean >= -6:
text_neg_mean = emotion_level2
else:
text_neg_mean = emotion_level1
if total_mean <= 6 and total_mean >= -6:
text_total_mean = emotion_level3
elif total_mean > 6:
text_total_mean = emotion_level4
else:
text_total_mean = emotion_level2
##################################正负进行方差计算
pos_variance = pos_array.var(axis=0)
neg_variance = neg_array.var(axis=0)
total_variance = total_array.var(axis=0)
var_ratio = pos_variance/neg_variance
#print "pos_variance:", pos_variance, "neg_variance:", neg_variance, "var_ration:", var_ratio
if total_variance > 10: # 赋予不同的情感波动级别
text_total_var = emotion_level7
else:
text_total_var = emotion_level6
################################构成字典返回
result_dict = {}
result_dict['pos_number'] = pos_number # 正向微博数
result_dict['neg_number'] = neg_number # 负向微博数
result_dict['mid_number'] = mid_number # 中性微博数
result_dict['number_ratio'] = round(number_ratio, 1) # 正负微博数之比，保留一位小数四舍五入
result_dict['pos_mean'] = round(pos_mean, 1) # 积极情感平均分
result_dict['neg_mean'] = round(neg_mean, 1) # 消极情感平均分
result_dict['total_mean'] = round(total_mean, 1) # 总的情感平均得分
result_dict['mean_ratio'] = abs(round(mean_ratio, 1)) # 积极情感平均分/消极情感平均分
result_dict['pos_variance'] = round(pos_variance, 1) # 积极得分方差
result_dict['neg_variance'] = round(neg_variance, 1) # 消极得分方差
result_dict['total_variance'] = round(total_variance, 1) # 总的情感得分方差
result_dict['var_ratio'] = round(var_ratio, 1) # 积极得分方差/消极得分方差
result_dict['text_pos_number'] = text_pos_number # 各种情感评价
result_dict['text_neg_number'] = text_neg_number
result_dict['text_mid_number'] = text_mid_number
result_dict['text_pos_mean'] = text_pos_mean
result_dict['text_neg_mean'] = text_neg_mean
result_dict['text_total_mean'] = text_total_mean
result_dict['text_total_var'] = text_total_var
"""
for key in result_dict.keys():
print 'key = %s , value = %s ' % (key, result_dict[key])
"""
return result_dict
if __name__ == '__main__':
results = run_score() # 计算每句话的极性得分，返回list，元素是（得分，微博）
write_results(results) # 将每条微博的极性得分都写入文件
result_dict = handel_result(results) # 计算结果的各种参数，返回字典
打分结果如图，即前面是情感得分，后面是语句：
