1、資料是使用scrapy-redis爬取的，存放在redis裡面，爬取的是最近大熱電影《海王》
2、使用了jieba中文分詞解析庫
3、使用了停用詞stopwords，過濾掉一些無意義的詞
4、使用matplotlib+wordcloud繪圖展示

from redis import Redis
import json
import jieba
from wordcloud import WordCloud
import matplotlib.pyplot as plt

# 載入停用詞
# stopwords = set(map(lambda x: x.rstrip('\n'), open('chineseStopWords.txt').readlines()))
stopwords = set()
with open('chineseStopWords.txt') as f:
    for line in f.readlines():
        stopwords.add(line.rstrip('\n'))
    stopwords.add(' ')
    # print(stopwords)
    # print(len(stopwords))

# 讀取影評
db = Redis(host='localhost')
items = db.lrange('review:items', 0, -1)
# print(items)
# print(len(items))

# 統計每個word出現的次數
# 過濾掉停用詞
# 記錄總數，用於計算詞頻
words = {}
total = 0

for item in items:
    data = json.loads(item)['review']
    # print(data)
    # print('------------')
    for word in jieba.cut(data):
        if word not in stopwords:
            words[word] = words.get(word, 0) + 1
            total += 1

print(sorted(words.items(), key=lambda x: x[1], reverse=True))
# print(len(words))
# print(total)

# 詞頻
freq = {k: v / total for k, v in words.items()}
print(sorted(freq.items(), key=lambda x: x[1], reverse=True))

# 詞雲
wordcloud = WordCloud(font_path='simhei.ttf',
                      width=500,
                      height=300,
                      scale=10,
                      max_words=200,
                      max_font_size=40).fit_words(frequencies=freq)  # Create a word_cloud from words and frequencies

plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.show()
 

繪圖結果：

參考：
https://github.com/amueller/word_cloud
http://amueller.github.io/word_cloud/