数据处理——SnowNLP计算文本情感值_snownlp库进行情感分析是怎么计算的

        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 Side\n(eg. pray,eulogize and suggestion)', 'Negative Side\n(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：