wc.py

import MeCab as mc
from matplotlib import pyplot as plt
from wordcloud import WordCloud

def mecab_analysis(text):
    t = mc.Tagger('-Ochasen -d /usr/local/lib/mecab/dic/mecab-ipadic-neologd/')
    t.parse('')
    node = t.parseToNode(text)
    output = []
    while node:
        word_type = node.feature.split(',')[0]
        if word_type in ['形容詞', '動詞','名詞', '副詞']:
            output.append(node.feature.split(',')[6])
        node = node.next
        if node is None:
            break
    return output

def create_wordcloud(words):
    font_path = '/Users/quanon/Library/Fonts/RictyDiminished-Regular.ttf'
    stopwords = ['てる', 'いる', 'なる', 'れる', 'する', 'ある', 'こと', 'これ', 'さん', 'して',
                 'くれる', 'やる', 'くださる', 'そう', 'せる', 'した',  '思う',
                 'それ', 'ここ', 'ちゃん', 'くん', '', 'て','に','を','は','の', 'が', 'と', 'た', 'し', 'で',
                 'ない', 'も', 'な', 'い', 'か', 'ので', 'よう', '']
    wordcloud = WordCloud(background_color='white', font_path=font_path,
                          width=900, height=500, stopwords=stopwords).generate(' '.join(words))

    plt.figure(figsize=(15, 12))
    plt.imshow(wordcloud)
    plt.axis('off')
    plt.show()

if __name__ == '__main__':
    with open('flamingo.txt') as f:
        text = f.read()
    words = mecab_analysis(text.replace('\n','').replace('\u3000',''))
    print(words)
    create_wordcloud(words)