def Get_Movie_Comment(url):
html = requests.get(url).content
page = etree.HTML(html)
comment_url = page.xpath(
"//*[@id='comments-section']/div[1]/h2/span/a/@href")[0]
print("Comment_URL : " + comment_url)
html = requests.get(comment_url).content
page = etree.HTML(html)
filename = 'jieba_data.txt'
with open(filename, 'w', encoding="utf8"
) as f: # 如果filename不存在会自动创建， 'w'表示写数据，写之前会清空文件中的原有数据！
comment_info = []
info = page.xpath("//*[@id='comments']")[0]
for k in range(1, 21):
# comment = {}
# comment ['name'] = info.xpath("./ div["+str(k) + "] / div[2] / h3 / span[2] / a /text()")[0]
# comment['href'] = info.xpath("./ div["+str(k) + "] / div[2] / h3 / span[2] / a /@href")[0]
comment = info.xpath("./ div[" + str(k) +
"] / div[2] / p/text()")[0]
f.write(comment + "\n")
# comment_info.append(comment)
print("=======================================================")
print(comment)
text_from_file_with_apath = open('jieba_data.txt', encoding='UTF-8').read()
wordlist_after_jieba = jieba.cut(text_from_file_with_apath, cut_all=True)
wl_space_split = " ".join(wordlist_after_jieba)
wc = WordCloud()
wc.font_path = "simhei.ttf" # 黑体e
my_wordcloud = wc.generate(wl_space_split)
plt.imshow(my_wordcloud)
plt.axis("off")
plt.show()
return comment_info
def build_word_cloud(text,back_coloring_path=None,font_path=None,txt_freq=None,scale=5):
"""
text: 词云的内容
back_coloring_path: 使用背景图片
font_path: 使用的字体所在路径
txt_freq: 词云权重,覆盖词云的内容
scale: 图片清晰度
"""
# 设置词云属性
wc = WordCloud(
background_color="white", # 背景颜色
max_words=2000, # 词云显示的最大词数
max_font_size=100, # 字体最大值
random_state=42,
scale=scale,
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
)
wc.font_path = font_path if font_path else 'SIMHEI.TTF'
if back_coloring_path:
back_coloring = imread(back_coloring_path)
wc.mask = back_coloring
if text:
wc.generate(text)
if txt_freq:
wc.generate_from_frequencies(txt_freq)
if back_coloring_path:
image_colors = ImageColorGenerator(back_coloring)
plt.figure()
plt.imshow(wc)
plt.axis("off")
plt.show()
# 绘制词云
wc.to_file('WordCloudDefautColors.png')
if back_coloring_path:
image_colors = ImageColorGenerator(back_coloring)
plt.imshow(wc.recolor(color_func=image_colors))
else:
plt.imshow(wc)
plt.axis("off")
# 绘制背景图片为颜色的图片
plt.figure()
if back_coloring_path:
plt.imshow(back_coloring, cmap=plt.cm.gray)
else:
plt.imshow(wc)
plt.axis("off")
plt.show()
# 保存图片
wc.to_file('WordCloudColorsByImg.png')
def generate_word_cloud(word_text, stopword_list=list(), is_chinese=False):
stopword_set = set(STOPWORDS)
for item in stopword_list:
stopword_set.add(item)
word_cloud = WordCloud(
# mask=np.array(Image.open('assert/background.png')),
stopwords=stopword_set,
width=1000,
height=618,
max_words=3000,
prefer_horizontal=0.99,
# random_state=2333
# max_font_size=200,
background_color='#EEEEEE',
)
if is_chinese:
word_cloud.font_path = '../assert/msyh.ttf'
word_cloud.max_words = 1000
word_text = ' '.join(jieba.cut(word_text))
word_cloud.generate(word_text)
return word_cloud
def make_word_cloud(content):
# read the mask image
d = path.dirname(__file__)
# alice_mask = np.array(Image.open(path.join(d, "mask/terran.jpg")))
mask = np.array(Image.open(path.join(d, mask_img)))
# font__dir = '/var/www/FlaskApp/FlaskApp/word_cloud_min/_fonts/lth.ttf'
# font__dir = 'C:\Users\zjsep_000\PycharmProjects\myDrone\word_cloud_min\_fonts\lth.ttf'
# font__dir = '_fonts/lth.ttf'
wc = WordCloud(background_color="white", max_words=1000, mask=mask)
# give the absolute dir for font ttf file
# wc.font_path = 'C:\Users\JI\Documents\GitHub\PycharmProjects\myDrone\word_cloud\_fonts\lth.ttf'
wc.font_path = abs_font_dir
# wc.font_path = 'C:\Users\zjsep_000\PycharmProjects\myDrone\word_cloud_min\_fonts\lth.ttf'
# wc.font_path = '_fonts/lth.ttf'
# wc.font_path = '/var/www/FlaskApp/FlaskApp/word_cloud_min/_fonts/lth.ttf'
# brush options: {'shoujin_brush.ttf','Japan_brush.ttf','qingke_fangzheng.ttf','KouzanBrushFont.ttf'}
# serfi-fonts:[]
wc.generate_from_frequencies(content)
# generate word cloud
# wc.generate(text)
# store to file
wc.to_file(path.join(d, "img/output.png"))
# store to static foder in web server
# wc.to_file(path.join(d, "../static/output.png"))
# show
plt.imshow(wc)
plt.axis("off")
plt.figure()
plt.imshow(mask, cmap=plt.cm.gray)
plt.axis("off")
plt.show()
def poltword(p):
#dftolist(data) is text list
tags = jieba.analyse.extract_tags(cf.dftostr(data[p]), topK=20)
print("\n".join(tags))
#words_ls = jieba.cut(cf.dftostr(data[p]), cut_all=True)
#words_split1 = " ".join(words_ls)
words_split2 = " ".join(tags)
wc1 = WordCloud(width=1980, height=1680)
wc1.font_path = "msyh.ttf "
wc2 = WordCloud(
width=400,
height=200,
background_color="#ffffff", # 设置背景颜色
max_words=500, # 词的最大数（默认为200）
max_font_size=60, # 最大字体尺寸
min_font_size=10, # 最小字体尺寸（默认为4）
colormap='bone', # string or matplotlib colormap, default="viridis"
random_state=10, # 设置有多少种随机生成状态，即有多少种配色方案
font_path='simhei.ttf')
#my_wordcloud1 = wc1.generate(words_split1)
my_wordcloud2 = wc2.generate(words_split2)
plt.imshow(my_wordcloud2)
l = int(100.0 * float(random.randint(40, 140)) / 255.0) # 在此处修改亮度
return "hsl({}, {}%, {}%)".format(h, s, l)
content = open('lyrics.txt', encoding='utf-8').read()
p = re.compile(r'\w*', re.L)
newcontent = p.sub('', content)
newcontent = newcontent.replace('.', '')
newcontent = newcontent.replace('作词', '')
newcontent = newcontent.replace('作曲', '')
final = jieba.cut(newcontent, cut_all=False)
space_split = " ".join(final)
d = path.dirname(__file__)
# background = imread(path.join(d, "heart.png")) # 可自定义背景图在cloudy内添加mask=background一项
cloudy = WordCloud(
background_color="white", #背景颜色
max_words=250, # 词云显示的最大词数,
max_font_size=50, #字体最大值
random_state=30,
scale=2,
relative_scaling=0.5,
color_func=random_color_func,
)
cloudy.font_path = "Consolas+YaHei+hybrid.ttf"
my_wordcloud = cloudy.generate(space_split)
plt.imshow(my_wordcloud)
plt.axis("off")
plt.savefig('wordcloud.png', dpi=1000)
plt.show()
#coding:utf-8
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import jieba
background_Image = plt.imread('')
text_from_file_with_apath = open('text.txt', 'rb').read()
wordlist_after_jieba = jieba.cut(text_from_file_with_apath, cut_all=True)
wl_space_split = " ".join(wordlist_after_jieba)
my_wordcloud = WordCloud(background_color="white",
margin=0,
width=512,
height=512,
mask=background_Image,
max_words=2000,
max_font_size=64,
random_state=42).generate(wl_space_split)
my_wordcloud.font_path = 'msyh.ttf'
plt.imshow(my_wordcloud)
plt.axis("off")
plt.show()
def generate_wordcloud(text_path,
mask_path=None,
width=400,
height=400,
lan='en',
font_path=None,
want_worlds=[],
stop_words=[],
path_to_save='.'):
"""
generate a word cloud of the mask picture you provide and a word cloud color by your picture
:param text_path: use to generate words
:param mask_path: picture you want to show
:param width: the width of the word cloud picture, if mask_path is not provided
:param height: the height of the word cloud picture, if mask_path is not provided
:param lan: the language of your text
:param font_path: if lan is 'cn', a chinese font must provide
:param want_worlds: the special word you don't want to separate
:param stop_words: the words you don't want to show up in your word cloud
:param path_to_save: the directory you want to save your word cloud (!! is directory not file)
:return: no return
"""
image_colors = None
wc = WordCloud(background_color='white',
max_words=1000,
max_font_size=400,
random_state=42)
# check path
if not os.path.isfile(text_path):
print('## text_path is invalid !!')
return
if lan == 'cn':
# if the lan is cn, then the path of chinese font path can't be null
if not os.path.isfile(font_path):
print('## chinese font_path cannot be null !!')
return
text = _generate_cn_words(text=open(text_path).read(),
want_words=want_worlds,
stop_words=stop_words)
wc.font_path = font_path
else:
text = open(text_path).read()
if not os.path.isfile(font_path):
wc.font_path = font_path
if mask_path is None and width > 0 and height > 0:
wc.height = height
wc.width = width
elif os.path.isfile(mask_path):
mask = np.array(Image.open(mask_path))
wc.mask = mask
image_colors = ImageColorGenerator(mask)
else:
print('## mask_path is invalid !!')
return
wc.generate(text=text)
plt.imshow(wc.recolor(color_func=image_colors))
plt.axis('off')
plt.show()
if os.path.isdir(path_to_save):
wc.to_file(os.path.join(path_to_save, 'words.png'))
else:
print('## path_to_save is invalid !!')
return
if mask_path is not None:
img1 = Image.open(os.path.join(path_to_save, 'words.png'))
img2 = Image.open(mask_path)
width = img1.size[0]
height = img1.size[1]
for i in range(0, width):
for j in range(0, height):
data1 = (img1.getpixel((i, j)))
data2 = (img2.getpixel((i, j)))
if (data1[0] <= 250 or data1[1] <= 250 or data1[2] <= 250):
img1.putpixel((i, j), (data2[0], data2[1], data2[2], 255))
# if (data1[0] == 255
# and data1[1] == 255
# and data1[2] == 255):
# img1.putpixel((i, j), (205, 205, 205, 255))
plt.imshow(img1)
plt.axis('off')
plt.show()
img1.save(os.path.join(path_to_save, 'wordcloud.png'))
creat_folder(f_path)
lyric_path = f_path
foler_path = f_path + '/'
#singer_id=2116#eason
start_url = 'http://music.163.com/artist?id={}'.format(singer_id)
html = get_html(start_url)
singer_infos = get_singer_info(html)
#获取歌词，同时判断是否有歌词（纯音乐没有歌词），没有歌词会导致程序异常停止（没有歌词就没有必要获取歌词了）
for singer_info in singer_infos:
lyric = get_lyric(singer_info[1])
if lyric == None:
continue
write_text(singer_info[0], lyric, foler_path)
all_lyric(lyric_path)
os.chdir(lyric_path)
text = ''
#将所有的歌词拼成一个字符串，方便后面进行分词
with open('all_lyric.txt', 'r', encoding='utf-8') as f:
for line in f:
text += line
words_ls = jieba.cut(text, cut_all=True) #利用jieba进行分词
words_split = ' '.join(words_ls)
wc = WordCloud(width=1980, height=1680)
wc.font_path = "simhei.ttf"
my_wordcloud = wc.generate(words_split) #生成词云
plt.imshow(my_wordcloud)
plt.axis("off") #关闭坐标轴
plt.savefig('lyric') #save image
plt.show()
# print('end')
def unknowncoll(filename='unknownwords.p', stem=False):
"""
Word cloud from sentiment analysis.
Finds the bi-collocation of unknown words (words without sentiment)
and displays the 10 most common words based on frequency in a word-cloud,
colored green for words seen mostly in positive sentiments and red
for the opposite. Comparison is made on all comments concatenated
-> filename: name of the file to load unknown words from
-> stem: stem the words
"""
bigram_measures = nltk.collocations.BigramAssocMeasures()
subreddits = scraper.load_data('sub-reddits.txt')
fullcomment = []
print 'building comment'
for name, data in subreddits.items():
for sub_id, sub in data.items():
fullcomment += [fixer(comment, True, stem).split() for comment in sub.comments]
print 'getting unknowns'
unknownwords = unknownsent(filename)
#flatten the comment structure
fullcomment = [word for comment in fullcomment for word in comment]
basefinder = BigramCollocationFinder.from_words(fullcomment)
count = 0
for unknown, unknownscore in unknownwords:
finder = copy.copy(basefinder)
print '\n' + unknown
#only bigrams that contain the unknown word
finder.apply_ngram_filter(lambda w1, w2: unknown != w1 and unknown != w2)
wordcloud = WordCloud()
wordcloud.font_path = 'C:\Windows\Fonts\comic.ttf'
#trick the wordcloud to accept custom input
wordcloud.generate('generate')
colls = finder.score_ngrams(bigram_measures.raw_freq)
colls = colls[:10]
maximum = colls[1][1]
#generate the tuple (word, score) for the wordcloud
cloudwords = [(word, score) for ((word, _), score) in colls if word != unknown]
cloudwords += [(word, score) for ((_, word), score) in colls if word != unknown]
#normalize the scores
cloudwords = [(word, score / maximum) for (word, score) in cloudwords]
#tricking part 2.
wordcloud.fit_words(cloudwords)
wordcloud.to_image()
if(unknownscore > 0):
wordcloud = wordcloud.recolor(color_func=green_color_func, random_state=3)
else:
wordcloud = wordcloud.recolor(color_func=red_color_func, random_state=3)
count += 1
plt.figure(count)
plt.title(unknown)
plt.imshow(wordcloud)
plt.axis("off")
plt.savefig('plots/' + unknown + '.png', bbox_inches='tight')
plt.close()