python系列之（4）豆瓣图书《平凡的世界》书评及情感分析

摘要：

本文主要捕捉和分析豆瓣《平凡的世界》一书的短评，并使用snowNLP分析其情感。p=“+strprinthttp=urllib3.PoolManager（）time.sleetry:r=http.requestplain_text=r.data.decode（）printexceptExceptionase:printscontinuesoup=BeautifulSoupligroup=sound.find_allforiteminligroup:try:comment=item.find.a.getspanlists=listwhile”“inspanlists:spanlists.remove（”“）if：stars=spanlist[1].getstars=switch_casecommenttime=spanlists[2]。strillerse:stars=0commenttime=spanlists[1].stringcontent=项查找。get_text（）add_commentexceptExceptionase:print_continuepage_num+=1ifpage_num˃999：breakdefswitch_Case:switcher={“建议”：5，“推荐”：4，“不坏”：3，“差”：2，“非常坏”：1}returnswitcher。getdefadd_comment:conn=sqlite3.connectcursor=conn.cursor（）游标。execute（“插入注释值（null，？）”，）cursor.close（）连接commit（）连接。在close（）获取之后，您可以在表中看到数据sqlite˃selectcountfromcomment；8302sqlite˃selectstar，从commentgroupystar开始计数；0 | 13591 | 582 | 1333 | 6434 | 18755 | 4234sqlite˃从评论者中选择*，按iddeslimit5；8302|斯坦利|4|2014-11-19|经典中的经典8301|这里的杰瑞|4|2016-03-08|非凡的8300|麦田里的沉睡者|5|2012-08-12|这本小说是我在大学里读的第一本，它给我带来了无与伦比的震撼。它把我从高中时读的那些yy小说中彻底唤醒了。

本篇主要是通过对豆瓣图书《平凡的世界》短评进行抓取并进行分析，并用snowNLP对其进行情感分析。

用到的模块有snowNLP，是一个python库，用来进行情感分析。

1.抓取数据

我们把抓取到的数据存储到sqlite,先建表，结构如下：

CREATE TABLE comment(
   id  integer PRIMARY KEY autoincrement     NOT NULL,
   commentator             VARCHAR(50)    NOT NULL,
   star           INTEGER   NOT NULL,
   time          VARCHAR(50)    NOT NULL,
   content       TEXT NOT NULL
   );

然后写python代码进行抓取，如下：

import sys
from os import path
import time
import urllib3
import requests
import numpy as np
import sqlite3
from bs4 import BeautifulSoup
from urllib import parse
from snownlp import SnowNLP
import matplotlib.pyplot as plt
import jieba
from wordcloud import WordCloud
from PIL import Image

headers=[{'User-Agent':'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US; rv:1.9.1.6) Gecko/20091201 Firefox/3.5.6'},
{'User-Agent':'Mozilla/5.0 (Windows NT 6.2) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.12 Safari/535.11'},
{'User-Agent': 'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.2; Trident/6.0)'}]

def get_comment():
    page_num = 0;
    total_num = 0;
    while(1):
        page_num +=1
        url = "https://book.douban.com/subject/1200840/comments/hot?p="+str(page_num)
        print(url)
        http = urllib3.PoolManager()
        time.sleep(np.random.rand()*5)
        try:
            r = http.request("GET", url, headers=headers[page_num%len(headers)])
            plain_text = r.data.decode()
            print(plain_text)
        except Exception as e:
            print(e)
            continue
 
        soup = BeautifulSoup(plain_text, features="lxml")
        ligroup = soup.find_all("li", class_="comment-item")

        for item in ligroup:
            try:
                commentator = item.find("div", class_="avatar").a.get("title")
                spanlists = list(item.find("div", class_="comment").find("span", class_="comment-info"))
                while "
" in spanlists:
                    spanlists.remove("
")
                
                if (len(spanlists) == 3) :
                    stars = spanlists[1].get("title")
                    stars = switch_case(stars)
                    commenttime = spanlists[2].string
                else:
                    stars = 0
                    commenttime = spanlists[1].string

                content = item.find("span", class_="short").get_text()
                add_comment(commentator, stars, commenttime, content)
            except Exception as e:
                print(e)
                continue
        page_num+=1
        if page_num > 999:
            break

def switch_case(value):
    switcher = {
        "力荐":5,
        "推荐":4,
        "还行":3,
        "较差":2,
        "很差":1
    }
    return switcher.get(value, 0)

def add_comment(commentator, star, time, content):
    conn = sqlite3.connect("spider.db")
    cursor = conn.cursor()
    cursor.execute("insert into comment values (null, ?, ?, ?, ?)", (commentator, star, time, content))
    cursor.close()
    conn.commit()
    conn.close()

抓取完之后可以在表中看到数据

sqlite> select count(1) from comment;
8302
sqlite> select star,count(1) from comment group by star;
0|1359
1|58
2|133
3|643
4|1875
5|4234
sqlite> select * from comment order by id desc limit 5;
8302|燊栎|4|2014-11-19|经典中的经典
8301|Jerryhere|4|2016-03-08|平凡中的不平凡
8300|麦田睡觉者|5|2012-08-12|这部小说是我上大学看的第一本小说，它带给我的震撼是无与伦比的。彻底将我从高中时看的那些yy小说里震醒。同时，它真的是一部非常好看的小说，平凡的世界里不平凡的人生，生命总是充满苦痛伤悲，这些苦难让生命愈发的沉重厚实
8299|朔望|0|2013-07-29|人生就是如此平凡
8298|mindinthesky|0|2012-09-17|不错，中国就是这样子

2.简单分析

数据抓取完了之后我们进行简单分析，看下各个星的占比

python系列之（4）豆瓣图书《平凡的世界》书评及情感分析第1张

然后在把所有的comment导到文件中，进行词云分析

导出如下：

>sqlite3 -header -csv spider.db "select content from comment;" > commentall.csv

完了就会在当前目录下生成一个commentall.csv的文件

然后可以对其词云分析，代码如下：

def make_cloud():
    text = open('commentall.txt', 'r', encoding='utf-8').read()
    cut_text = jieba.cut(text)
    result = " ".join(cut_text)
    wc = WordCloud(
        font_path='Deng.ttf',     #字体路劲
        background_color='white',   #背景颜色
        width=2000,
        height=1200,
        max_font_size=100,            #字体大小
        min_font_size=10,
        mask=plt.imread('timg.jpeg'),  #背景图片
        max_words=1000
    )
    wc.generate(result)
    wc.to_file('jielun.png')    #图片保存

    plt.figure('jielun')   #图片显示的名字
    plt.imshow(wc)
    plt.axis('off')        #关闭坐标
    plt.show()

分析完之后的图片输出是下图：

python系列之（4）豆瓣图书《平凡的世界》书评及情感分析第2张

3.情感分析

我们从打的五星和一星能清楚的看到情感，但是对零星的就不太好判断，现在主要是用snowNLP对零星的做情感分析。要想分析，就先得训练，因为目前的是针对电商的评论，不适合现在的场景，怎么训练呢？

首先，我们把5颗星的评论导出存为pos.txt,作为积极的评论，把1颗星的评论导出存为neg.txt作为消极的评论；

然后，利用pos.txt和neg.txt进行训练

最后，在利用训练完的模型对0颗星的进行分析

好了，开始吧

首先导出

>sqlite3 -header -csv spider.db "select conent from comment where star = 5 limit 100;" > pos.csv

>sqlite3 -header -csv spider.db "select conent from comment  where star = 1 limit 100;" > neg.csv

然后找到snownlp的安装路径，如下方法：

kumufengchunMacBook-Pro:douban kumufengchun$ python
Python 3.6.4 (default, Jun  6 2019, 17:59:50)
[GCC 4.2.1 Compatible Apple LLVM 10.0.0 (clang-1000.10.44.4)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import snownlp
>>> snownlp
<module 'snownlp' from '/Users/kumufengchun/.pyenv/versions/3.6.4/lib/python3.6/site-packages/snownlp/__init__.py'>

找到了之后，把刚才的pos.scv和neg.csv拷贝到

/Users/kumufengchun/.pyenv/versions/3.6.4/lib/python3.6/site-packages/snownlp/sentiment/下边的pos.txt和neg.txt,为了保持名字一致，我们可以把之前的csv后缀的改为txt
然后开始用刚才导出的数据进行训练,训练完之后的输出保存为commentsentiment.marshal

from snownlp import sentiment
sentiment.train('neg.txt', 'pos.txt')
sentiment.save('commentsentiment.marshal')

然后把训练完输出的文件，在init文件中修改,文件训练完之后输出的是commentsentiment.marshal.3,后缀3是版本的意思，不用管他，在引用的时候不要加3，否则会报错，修改代码如下：

/Users/kumufengchun/.pyenv/versions/3.6.4/lib/python3.6/site-packages/snownlp/sentiment/__init__.py

data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                         'commentsentiment.marshal')

好了，训练完之后，我们可以进行简单的测试

from snownlp import SnowNLP

str = "好很好"
s = SnowNLP(str)
print(s.words)
print(s.tags)
print(s.sentiments)

输出如下：

['好', '很', '好']
<zip object at 0x124963588>
0.6088772592136402

4.用训练的模型进行情感分析

代码如下：

def get_comment_bypage(offset, limit):
    conn = sqlite3.connect("spider.db")
    cursor = conn.cursor()
    cursor.execute('select content from comment where star=0 limit ?,?', (offset, limit))
    values = cursor.fetchall()
    cursor.close()
    conn.close()
    return values


def analyse_sentiment():
    offset = 0
    limit = 50
    commentcounts = {}
    while (offset < 1400):
        comments = get_comment_bypage(offset, limit)
        for comment in comments:
            s = SnowNLP(''.join(comment))
            print(s.sentiments)
            sentiment = round(s.sentiments, 2)
            if sentiment in commentcounts:
                commentcounts[sentiment] += 1 
            else:
                commentcounts[sentiment] = 1
        offset+=limit
    print(commentcounts)
    return commentcounts

然后我们把所有的输出做个图如下：

python系列之（4）豆瓣图书《平凡的世界》书评及情感分析第3张

可以看到每个输出所占的数量，如何判断是积极还是消极呢，一般采取0.3，大于0.3的为积极，否则为消极，也可以把之前的数据都跑一遍，定义个区间。

完整的代码如下：

import sys
from os import path
import time
import urllib3
import requests
import numpy as np
import sqlite3
from bs4 import BeautifulSoup
from urllib import parse
from snownlp import SnowNLP
import matplotlib.pyplot as plt
import jieba
from wordcloud import WordCloud
from PIL import Image

headers=[{'User-Agent':'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US; rv:1.9.1.6) Gecko/20091201 Firefox/3.5.6'},
{'User-Agent':'Mozilla/5.0 (Windows NT 6.2) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.12 Safari/535.11'},
{'User-Agent': 'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.2; Trident/6.0)'}]

def get_comment():
    page_num = 0;
    total_num = 0;
    while(1):
        page_num +=1
        url = "https://book.douban.com/subject/1200840/comments/hot?p="+str(page_num)
        print(url)
        http = urllib3.PoolManager()
        time.sleep(np.random.rand()*5)
        try:
            r = http.request("GET", url, headers=headers[page_num%len(headers)])
            plain_text = r.data.decode()
            print(plain_text)
        except Exception as e:
            print(e)
            continue
 
        soup = BeautifulSoup(plain_text, features="lxml")
        ligroup = soup.find_all("li", class_="comment-item")

        for item in ligroup:
            try:
                commentator = item.find("div", class_="avatar").a.get("title")
                spanlists = list(item.find("div", class_="comment").find("span", class_="comment-info"))
                while "
" in spanlists:
                    spanlists.remove("
")
                
                if (len(spanlists) == 3) :
                    stars = spanlists[1].get("title")
                    stars = switch_case(stars)
                    commenttime = spanlists[2].string
                else:
                    stars = 0
                    commenttime = spanlists[1].string

                content = item.find("span", class_="short").get_text()
                add_comment(commentator, stars, commenttime, content)
            except Exception as e:
                print(e)
                continue
        page_num+=1
        if page_num > 999:
            break

def switch_case(value):
    switcher = {
        "力荐":5,
        "推荐":4,
        "还行":3,
        "较差":2,
        "很差":1
    }
    return switcher.get(value, 0)

def add_comment(commentator, star, time, content):
    conn = sqlite3.connect("spider.db")
    cursor = conn.cursor()
    cursor.execute("insert into comment values (null, ?, ?, ?, ?)", (commentator, star, time, content))
    cursor.close()
    conn.commit()
    conn.close()


def get_comment_bypage(offset, limit):
    conn = sqlite3.connect("spider.db")
    cursor = conn.cursor()
    cursor.execute('select content from comment where star=0 limit ?,?', (offset, limit))
    values = cursor.fetchall()
    cursor.close()
    conn.close()
    return values


def analyse_sentiment():
    offset = 0
    limit = 50
    commentcounts = {}
    while (offset < 1400):
        comments = get_comment_bypage(offset, limit)
        for comment in comments:
            s = SnowNLP(''.join(comment))
            print(s.sentiments)
            sentiment = round(s.sentiments, 2)
            if sentiment in commentcounts:
                commentcounts[sentiment] += 1 
            else:
                commentcounts[sentiment] = 1
        offset+=limit
    print(commentcounts)
    return commentcounts


def make_cloud():
    text = open('commentall.txt', 'r', encoding='utf-8').read()
    cut_text = jieba.cut(text)
    result = " ".join(cut_text)
    wc = WordCloud(
        font_path='Deng.ttf',     #字体路劲
        background_color='white',   #背景颜色
        width=2000,
        height=1200,
        max_font_size=100,            #字体大小
        min_font_size=10,
        mask=plt.imread('timg.jpeg'),  #背景图片
        max_words=1000
    )
    wc.generate(result)
    wc.to_file('jielun.png')    #图片保存

    plt.figure('jielun')   #图片显示的名字
    plt.imshow(wc)
    plt.axis('off')        #关闭坐标
    plt.show() 
    

if __name__=='__main__':
    get_comment()
    analyse_sentiment()
    make_cloud()