Use Python to crawl the most dazzling Guoman Wushan Wuxing to see what 100000 netizens are saying


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Watching animation partners should know that recently a Shenman “Wushan Wuxing” was released, which was highly praised for its unique ink painting style and flaming fighting scenes. The first episode was broadcast in less than 24 hours, and the hot search of station B was the first, Douban was the first 9.5, the popularity can be seen. As far as fighting scenes are concerned, it’s not too much to say that it’s the most dazzling animation. Of course, the only shortcoming is that the number of episodes is a little small, with only 3 episodes.

After looking at the animation, do you think what I said is the most dazzling animation? It’s not empty words. Next, let’s crawl some comments to understand your views on this animation. Here we select three platforms: B station, microblog and Douban to crawl the data.

Crawling station B

Let’s first crawl the barrage data of station B. the animation link is: The bullet screen link is: The crawling code is as follows:

url = ""
req = requests.get(url)
html = req.content
html_ Doc = str (HTML, "UTF-8") #
soup = BeautifulSoup(html_doc, "lxml")
results = soup.find_all('d')
contents = [x.text for x in results]
#Save the results
dic = {"contents": contents}
df = pd.DataFrame(dic)
df["contents"].to_csv("bili.csv", encoding="utf-8", index=False)


If you don’t know about the data of crawling B station barrage, you can have a look: crawling B station barrage.

We then generate the word cloud from the crawled barrage data. The code implementation is as follows:

def jieba_():
    #Open comment data file
    content = open("bili.csv", "rb").read()
    #Jieba participle
    word_list = jieba.cut(content)
    words = []
    #Filtered words
    stopwords = open("stopwords.txt", "r", encoding="utf-8").read().split("\n")[:-1]
    for word in word_list:
        if word not in stopwords:
    global word_cloud
    #Separate words with commas
    word_cloud = ','.join(words)

def cloud():
    #Open the background image of word cloud
    cloud_mask = np.array("bg.png"))
    #Define some attributes of word cloud
    wc = WordCloud(
        #The background image segmentation color is white
        #Background pattern
        #Display the maximum number of words
        #Show Chinese
        #Maximum size
    global word_cloud
    #Word cloud function
    x = wc.generate(word_cloud)
    #Generate word cloud image
    image = x.to_image()
    #Show word cloud pictures
    #Save word cloud image



Take a look at the effect:

Crawling microblog

We then crawled the micro blog comments of animation. The target we chose was the comment data of this micro blog on top of Wushan five elements official blog, as shown in the figure:

The crawling code implementation is as follows:


#Crawl a page of comments
def get_one_page(url):
    headers = {
        'User-agent' : 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3880.4 Safari/537.36',
        'Host' : '',
        'Accept' : 'application/json, text/plain, */*',
        'Accept-Language' : 'zh-CN,zh;q=0.9',
        'Accept-Encoding' : 'gzip, deflate, br',
        'cookie':'Own cookie ',
        'DNT' : '1',
        'Connection' : 'keep-alive'
    #Get HTML
    response = requests.get(url, headers = headers, verify=False)
    #Crawling success
    if response.status_code == 200:
        #The return value is an HTML document, which is passed into the parsing function
        return response.text
    return None

#Parsing and saving comment information
def save_one_page(html):
    comments = re.findall('(.*?)', html)
    for comment in comments[1:]:
        result = re.sub('', '', comment)
        If 'reply @'not in result:
            with open('wx_comment.txt', 'a+', encoding='utf-8') as fp:

for i in range(50):
    url = ''+str(i) 
    html = get_one_page(url)
    Print ('crawling comments on page% d '% (I + 1))


For those unfamiliar with crawling microblog comments, you can refer to crawling microblog comments.

Similarly, we will generate a word cloud from comments to see the effect:
Crawling Douban

Finally, we crawled the Douban review data of animation. The Douban address of animation is: The implementation code of crawling is as follows:

def spider():
    url = ''
    headers = {"User-Agent": 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0)'}
    #Comment website, in order to turn the page dynamically, the formatted number is added after the start, the short review page has 20 pieces of data, each page adds 20 pieces
    url_comment = ''
    data = {
        'ck': '',
        'name':'user name ',
        'password':'password ',
        'remember': 'false',
        'ticket': ''
    session = requests.session(), headers=headers, data=data)
    #Initialize four lists to store user name, comment star, time and comment text
    users = []
    stars = []
    times = []
    content = []
    #Grab 500, 20 per page, which is the upper limit of Douban
    for i in range(0, 500, 20):
        #Get HTML
        data = session.get(url_comment % i, headers=headers)
        #Status code 200 indicates success
        Print ('page ','I','status Code: ', data.status_ code)
        #Pause for 0-1 second to prevent IP from being blocked
        #Parsing HTML
        selector = etree.HTML(data.text)
        #Get all comments on a single page with XPath
        comments = selector.xpath('//div[@class="comment"]')
        #Traverse all comments for details
        for comment in comments:
            #Get user name
            user = comment.xpath('.//h3/span[2]/a/text()')[0]
            #Get star reviews
            star = comment.xpath('.//h3/span[2]/span[2]/@class')[0][7:8]
            #Acquisition time
            date_time = comment.xpath('.//h3/span[2]/span[3]/@title')
            #Some time is empty, need to judge
            if len(date_time) != 0:
                date_time = date_time[0]
                date_time = date_time[:10]
                date_time = None
            #Get comment text
            comment_text = comment.xpath('.//p/span/text()')[0].strip()
            #Add all information to the list
    #Packing with dictionaries
    comment_dic = {'user': users, 'star': stars, 'time': times, 'comments': content}
    #Convert to dataframe format
    comment_df = pd.DataFrame(comment_dic)
    #Save data
    #Save the comments separately
    comment_df['comments'].to_csv('comment.csv', index=False)



For those unfamiliar with crawling Douban comments, you can refer to crawling Douban comments.

Take a look at the generated word cloud effect:

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