对文本进行情感值计算的简单方法，使用python中的SnowNLP。但是，在此之前需要对原始模型进行训练。
训练代码如下：
import sys
#reload(sys) sys.setdefaultencoding('utf-8')
import pandas as pd #加载pandas
text=pd.read_csv(u'D:文本.csv',header=0) #读取文本数据
text0=text.iloc[:,0] #提取所有数据
text1=[i.encode('utf-8') for i in text0]
print(text1)
from snownlp import sentiment #加载情感分析模块
sentiment.train('E:/anaconda/Lib/site-packages/snownlp/sentiment/neg.txt', 'E:/anaconda/Lib/site-packages/snownlp/sentiment/pos.txt') #对语料库进行训练，把路径改成相应的位置。
sentiment.save('D:/sentiment.marshal')
然后对训练好的模型进行调用，并绘制出饼状图。
from snownlp import SnowNLP
import matplotlib.pyplot as plt
import re
import csv
comment = []
pos_count = 0
neg_count = 0
for line_data in open("D:文本.csv",encoding='utf-8'):
comment = line_data
s = SnowNLP(comment)
rates = s.sentiments
print(rates)
if (rates >= 0.5):
pos_count += 1
elif (rates < 0.5):
neg_count += 1
else :
pass
with open("D:/文本处理.csv", "a", encoding="gbk") as fp:
fp.write("%s" % rates+"n")
labels = 'Positive Siden(eg. pray,eulogize and suggestion)', 'Negative Siden(eg. abuse,sarcasm and indignation)'
fracs = [pos_count,neg_count]
explode = [0.1,0] # 0.1 凸出这部分，
plt.axes(aspect=1)
plt.pie(x=fracs, labels=labels, explode=explode,autopct='%3.1f %%',
shadow=True, labeldistance=1.1, startangle = 90,pctdistance = 0.6)
plt.savefig("emotions_pie_chart.jpg",dpi = 360)
plt.show()
计算结果显示如下图：
绘制的饼状图如下所示：
