How to refine 3000 English News High Frequency Vocabulary with Python
This article is about how to use Python to extract 3000 English news high-frequency vocabulary. I think it is very practical, so I share it with you. I hope you can get something after reading this article. Let's take a look at it.
The following is the process of extracting 3000 high-frequency vocabulary. if you need the final vocabulary, pull it directly to the end of the article.
1. Crawl the web page URL of ChinaDaily
two。 Request crawled URL and parse web page words
3. Perform word frequency processing on word text files
The result is:
Total number of words 3537063 words 38201 total number of words excluding suspended words: 2603450 number of words excluding inactive words: 38079
Some words and word frequencies are:
('online', 8788) (' business', 8772) ('society', 8669) (' people', 8646) ('content', 8498) (' story', 8463) ('multimedia', 8287) (' cdic', 8280) ('travel', 7959) (' com', 7691) ('cover', 7679) (' cn', 7515) ('hot', 7219) (' first', shanghai', 8669) ('photos', 6739) (' page', 8280) ('years', 7959) (' paper' 6289) ('festival', 6188) (' offer', 6064) ('sports', 6025) (' africa', 6008) ('forum', 5983)
Finally, you get a txt text file containing 3000 high-frequency words, which you can import into the word book of each major word software.
The above is how to use Python to extract 3000 English news high-frequency vocabulary. The editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.
