情感分析（判断文章正负向）

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。

程序文件分布：

代码1：

# -*- coding: utf-8 -*-
import jieba
import jieba.posseg as pseg
print ("加载用户词典...")
jieba.load_userdict('C:/Anaconda3/Lib/site-packages/jieba/pos_dict.txt')
jieba.load_userdict('C:/Anaconda3/Lib/site-packages/jieba/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.encode('utf8').decode('utf8')
	start = 0
	i = 0
	token = 'meaningless'
	sents = []
	punt_list = ',.!?;~。！？；～… '.encode('utf8').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:])
		# print(words[start:])
	return sents

def read_lines(filename):
	fp = open(filename, 'rb')
	lines = []
	for line in fp.readlines():
		line = line.strip()
		line = line.decode("utf8")
		lines.append(line)
	fp.close()
	return lines

# 去除停用词
def del_stopwords(seg_sent):
	stopwords = read_lines("D://untitled/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("D://untitled/Sentiment_dict/degree_dict/most.txt")
	elif request == "two":
		result_dict = read_lines("D://untitled/Sentiment_dict/degree_dict/very.txt")
	elif request == "three":
		result_dict = read_lines("D://untitled/Sentiment_dict/degree_dict/more.txt")
	elif request == "four":
		result_dict = read_lines("D://untitled/Sentiment_dict/degree_dict/ish.txt")
	elif request == "five":
		result_dict = read_lines("D://untitled/Sentiment_dict/degree_dict/insufficiently.txt")
	elif request == "six":
		result_dict = read_lines("D://untitled/Sentiment_dict/degree_dict/inverse.txt")
	else:
		pass
	return result_dict

 

代码2：

 

# -*- coding: utf-8 -*-
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("D://untitled/Sentiment_dict/emotion_dict/pos_all_dict.txt")
negdict = tp.read_lines("D://untitled/Sentiment_dict/emotion_dict/neg_all_dict.txt")
# 程度副词词典
mostdict = tp.read_lines('D://untitled/Sentiment_dict/degree_dict/most.txt')   # 权值为2
verydict = tp.read_lines('D://untitled/Sentiment_dict/degree_dict/very.txt')   # 权值为1.5
moredict = tp.read_lines('D://untitled/Sentiment_dict/degree_dict/more.txt')   # 权值为1.25
ishdict = tp.read_lines('D://untitled/Sentiment_dict/degree_dict/ish.txt')   # 权值为0.5
insufficientdict = tp.read_lines('D://untitled/Sentiment_dict/degree_dict/insufficiently.txt')  # 权值为0.25
inversedict = tp.read_lines('D://untitled/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:
		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)


# 求单条文章语句的情感倾向总得分
def single_review_sentiment_score(weibo_sent):
	single_review_senti_score = [] # 定义一个空列表
	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: # 遍历分句
		seg_sent = tp.segmentation(sent)   # 分词
		seg_sent = tp.del_stopwords(seg_sent)[:]

		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
			if word in posdict:  # 如果是积极情感词
				poscount += 1   # 积极得分+1
				for w in seg_sent[s:i]:
					poscount = match(w, 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
				negcount += 1 # 消极得分+1
				for w in seg_sent[s:i]:
					negcount = match(w, negcount)
				s = i + 1 # 记录情感词位子变化

			# 如果是感叹号，表示已经到本句句尾
			elif word == "！".encode('utf8').decode("utf-8") or word == "!".encode('utf8').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   # 该条文章情感的最终得分
	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 中的所有文章，返回一个列表，列表中元素为（分值，文章）元组
def run_score():
	fp_test = open('D:/untitled/To_be_processed/chinese_weibo.txt', 'r')   # 待处理数据
	contents = []
	for content in fp_test.readlines():
		content = content.strip()
		content = content.encode('utf8').decode("utf8")
		contents.append(content)
	fp_test.close()
	results = []
	for content in contents:
		score = single_review_sentiment_score(content)  # 对每条文章调用函数求得打分
		results.append((score, content))   # 形成（分数，文章）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', 'a')
	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文章比，正负极性分值的平均值比，正负分数分别的方差
def handel_result(results):
	# 正极性文章数量，负极性文章数量，中性文章数量，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])
	#################################各种极性文章数量统计
	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 = "积极文章条数为 " + str(pos_number) + " 条，占全部文章比例的 %" + str(pos_number_ratio*100)
	text_neg_number = "消极文章条数为 " + str(neg_number) + " 条，占全部文章比例的 %" + str(neg_number_ratio*100)
	text_mid_number = "中性情感文章条数为 " + str(mid_number) + " 条，占全部文章比例的 %" + 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   # 正向文章数
	result_dict['neg_number'] = neg_number   # 负向文章数
	result_dict['mid_number'] = mid_number   # 中性文章数
	result_dict['number_ratio'] = round(number_ratio, 1)  # 正负文章数之比，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，文章）
	write_results(results)    # 将每条文章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
	print(result_dict)