爬取bilibili的弹幕制作词云（哔哩哔哩弹幕提取网站）

爬取哔哩哔哩的弹幕，http://comment.bilibili.com/6315651.xml

需要知道cid,可以F12,F5刷新，找cid，找到之后拼接url

也可以写代码，解析response获取cid,然后再拼接

使用requests或者urllib都可以

我是用requests，请求该链接获取到xml文件

代码：获取xml

def get_data():
 res = requests.get('http://comment.bilibili.com/6315651.xml')
 res.encoding = 'utf8'
 with open('gugongdanmu.xml', 'a', encoding='utf8') as f:
 f.writelines(res.text)

解析xml,

def analyze_xml():
 f1 = open("gugongdanmu.xml", "r", encoding='utf8')
 f2 = open("tanmu2.txt", "w", encoding='utf8')
 count = 0
 # 正则匹配解决xml的多余的字符
 dr = re.compile(r'<[^>]+>', re.S)
 while 1:
 line = f1.readline()
 if not line:
 break
 pass
 # 匹配到之后用空代替
 dd = dr.sub('', line)
 # dd = re.findall(dr, line)
 count = count+1
 f2.writelines(dd)
 print(count)

去掉无用的字符和数字，找出所有的汉字

def analyze_hanzi():
 f1 = open("tanmu2.txt", "r", encoding='utf8')
 f2 = open("tanmu3.txt", "w", encoding='utf8')
 count = 0
 # dr = re.compile(r'<[^>]+>',re.S)
 # 所有的汉字[一-龥]
 dr = re.compile(r'[一-龥]+',re.S)
 while 1:
 line = f1.readline()
 if not line:
 break
 pass
 # 找出无用的符号和数字
 # dd = dr.sub('',line)
 dd = re.findall(dr, line)
 count = count+1
 f2.writelines(dd)
 print(count)
 # pattern = re.compile(r'[一-龥]+')

使用jieba分词，生成词云

def show_sign():
 content = read_txt_file()
 segment = jieba.lcut(content)
 words_df = pd.DataFrame({'segment': segment})
 stopwords = pd.read_csv("stopwords.txt", index_col=False, quoting=3, sep=" ", names=['stopword'], encoding='utf-8')
 words_df = words_df[~words_df.segment.isin(stopwords.stopword)]
 print(words_df)
 print('-------------------------------')
 words_stat = words_df.groupby(by=['segment'])['segment'].agg(numpy.size)
 words_stat = words_stat.to_frame()
 words_stat.columns = ['计数']
 words_stat = words_stat.reset_index().sort_values(by=["计数"], ascending=False)
 # 设置词云属性
 color_mask = imread('ciyun.png')
 wordcloud = WordCloud(font_path="simhei.ttf", # 设置字体可以显示中文
 background_color="white", # 背景颜色
 max_words=1000, # 词云显示的最大词数
 mask=color_mask, # 设置背景图片
 max_font_size=100, # 字体最大值
 random_state=42,
 width=1000, height=860, margin=2,
 # 设置图片默认的大小,但是如果使用背景图片的话, # 那么保存的图片大小将会按照其大小保存,margin为词语边缘距离
 )
 # 生成词云, 可以用generate输入全部文本,也可以我们计算好词频后使用generate_from_frequencies函数
 word_frequence = {x[0]: x[1] for x in words_stat.head(1000).values}
 print(word_frequence)
 # for key,value in word_frequence:
 # write_txt_file(word_frequence)
 word_frequence_dict = {}
 for key in word_frequence:
 word_frequence_dict[key] = word_frequence[key]
 wordcloud.generate_from_frequencies(word_frequence_dict)
 # 从背景图片生成颜色值
 image_colors = ImageColorGenerator(color_mask)
 # 重新上色
 wordcloud.recolor(color_func=image_colors)
 # 保存图片
 wordcloud.to_file('output.png')
 plt.imshow(wordcloud)
 plt.axis("off")
 plt.show()

运行程序，结果：

统计的结果

完成！

pip的换源，原来的太慢，然后将你自己没有库装上