Reuters corpus is organized by overlapping categories. fileid = reuters.fileids() print(fileid,"\n" ,reuters.raw(fileid),"\n" ,reuters.categories(fileid),"\n") from rpus import reuters reuters.fileids()Ĭheck out one document's content, and its category. List all documents ids from the corpus we just downloaded. run Python code: import nltk nltk.download("reuters") In case you are not familiar with NLTK corpus, this article may be helpful to get NLTK started in less than one hour: Book Writing Pattern Analysis - Get start with NLTK and Python text analysis with a use case. I found the Reuters document corpus from NLTK is a good target for keyword extraction. Since I can’t use my daily work database here and also ensure you can perform the keywords extraction sample code in your local machine with minimum efforts. Steps to extract keywords from document corpus Target documents So, in theory, we should be able to leverage the text weight # to extract the most important words of a document.įor example, a document talks about scikit-learn should include much high density of keywords scikit-learn, while another document talks about “pandas” should have a high TF-IDF value for pandas. In both pure Python code and using scikit-learn package.īased on TF-IDF, those unique and important words should have high TF-IDF values in a certain document. In my previous article: Measure Text Weight using TF-IDF in Python and scikit-learn, I used a simple sample to show how to calculate the TF-IDF value for all words in a document. TF-IDF is a widely used algorithm that evaluates how relevant a word is to a document in a collection of documents. If you run a code block but welcomed with a missing import package error, this package must have been imported already somewhere ahead. Note that the code in this article was run and test in Jupyter Notebook. This article is logging how I extracted keywords, how it works, walkarounds, in Python. Capture new words and phrases automatically.Minimum manual interference and can automatically run.So, is there a solution that we can give documents tagging meet: The training data that right fit your dataset. You may say, why not using Machine Learning? like, Neral Network deep learning. Hiring a vendor company to do the tagging work is too much expensive. Manually tagging is unpractical Giving an existing tagging list will be outdated soon. You are scratching your head hard to giving tags to those random documents.
There were no tags when the data was generated. No matter it is customer support tickets, social media data, or community forum posts. Imagine you have millions(maybe billions) of text documents in hand.
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