Data visualization - White Snake 2: black snake robbery (3)

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7 Film review word cloud analysis

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​ edit   Stop word processing ：

  from PIL Import Image Pictures of the

Graph of word cloud operation results ： 

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Drink a bottle of Wangzai, let's continue ......

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 7 Film review word cloud analysis

download

pip install jieba  ( If a download fails , Just a few more times , If you can't, check online )

pip install jieba  

collections Statistical word quantity

import jieba
import wordcloud
import collections

lcurt Cut the string in a list

jieba.lcut(df[' Comment on '][0])

The operation result is as follows ： 

I have a stop phrase file （stopwords.txt）, You can also find resources on the Internet .

Put stop words in stop_words in , And put \n Cut off .

The code is shown as follows ：

with open('stopwords.txt','r',encoding='utf-8')as fp:
    words=fp.readlines()
    
stop_words = []
for word in words:
    w = word.strip('\n')# Put the backslash in the word n Cut off 
    stop_words.append(w)
stop_words

Operation result diagram ： 

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  Stop word processing ：

  Next, we begin to deal with stop words ,word_list = jieba.lcut(comment).

Then on word_list Do traversal , Remove the stop words （ You can go online to find stop words Resources ）

# Stop word processing 
good_words =[]

for comment in df[' Comment on ']:
    word_list = jieba.lcut(comment)
    
    # Yes word_list Do traversal , Remove the stop words （ Go online to find stop words Resources ）
    for word in word_list:
        if word not in stop_words:
            good_words.append(word)
        

In the following code, we carry out font type （ Mona Chao gang Heijian .ttf） Application , And configure the word number data to the word cloud object , Finally show the picture . The code is as follows ： 

c = collections.Counter(good_words)

wc = wordcloud.WordCloud(font_path=' Mona Chao gang Heijian .ttf',width=500,height=300,
                        background_color='white',
                        max_font_size=200,
                        min_font_size=5,
                        max_words=1000)
# Configure word number data to the word cloud object 
wc.generate_from_frequencies(c)

# Show the image 
plt.imshow(wc)

The operation results are as follows ： At this time, the size and color of word cloud are given by the system by default , As shown in the figure ：

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  from PIL Import Image Pictures of the

from PIL import Image

  Based on the last word cloud code , We added a love background picture to it , And define a color for word cloud from dark to light , Then turn off the horizontal and vertical coordinates beside the figure . The code is as follows ：

back_image = Image.open(r'C:\Users\1\Desktop\1.png')

c = collections.Counter(good_words)

# Adjust canvas 
plt.figure(figsize=(12,7))

wc = wordcloud.WordCloud(font_path=' Mona Chao gang Heijian .ttf',width=500,height=300,
                        background_color='white',
                         # The background color is white 
                        max_font_size=200,
                        min_font_size=5,
                          # Adjust the size of words to 5-200
                        max_words=1000,
                         # The maximum number of words that can be accommodated is 1000
                        mask=np.array(back_image),   
                         # Put pictures 
                        colormap=sns.dark_palette('pink',as_cmap=True)  
                         # The color of the tone cloud is a deep to light purple , Remember to set up as_cmap=True This parameter , Otherwise, the code cannot recognize this as a color parameter 
                        )
# Configure word number data to the word cloud object 
wc.generate_from_frequencies(c)

# Show the image 
plt.imshow(wc)
# Turn off the horizontal and vertical axes next to the diagram 
plt.axis('off')

Graph of word cloud operation results ： 

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So far, data visualization -《 White Snake 2： The green snake robbed 》 This concludes the analysis . Friends who feel useful give me a little praise , finger heart .