擅长:python、mysql、java
<p>我使用python方法得到了您所期望的结果</p>
<pre><code>import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
def wordcount(data):
cv = CountVectorizer(min_df=0, strip_accents="ascii", decode_error ="ignore")
counts = cv.fit_transform([data]).toarray().ravel()
words = np.array(cv.get_feature_names())
counts = map(int, counts)
words = [i.tolist() for i in words]
words = [x.encode('UTF8') for x in words]
unsorted_result = zip(words, counts)
print sorted(unsorted_result,key=lambda x: x[1], reverse=True)
</code></pre>
<p>将数据作为字符串发送到此函数</p>