KIDS CONTENT TAGGER HOW TO
KIDS CONTENT TAGGER PROFESSIONAL

Assume here that the corpus is found to have 10 topics, and we want 5 tags per text: from gensim.models import LdaModel, HdpModelĪssume further that we have a variable corpus, which is a preprocessed list of lists, with the subslist entries being word tokens. Although HDP does not require the number of topics as an input, the standard in such cases is still to use LDA with a derived topic number, as HDP can be problematic. With a general reference for preprocessing needed for these codes being found here, we can find tags through the following process using gensim.Ī heuristic way of deriving the optimal number of topics for LDA is found in this answer. This method provides strong results where tags generally represent the broader themes of the given texts. The top 70% of the words that define the first topic and 30% that define the second (without duplication) could then be considered as tags for the given text. Latent Dirichlet allocation or Hierarchical Dirichlet Process can be used to generate tags for individual texts within a greater corpus (body of texts) by extracting the most important words from the derived topics.Ī basic example would be if we were to run LDA over a corpus and define it to have two topics, and that we find further that a text in the corpus is 70% one topic, and 30% another. # select document keywords from the words at/near the top of this list: Term_importance = lambda word : 1.0/word_freq # obviously using a text larger than 50 or so words will give you more meaningful results # eg, what are the most common words in that text? # (just a DSU sort, there's a python built-in for this, but i can't remember it) # generate a 'word histogram' for the text-ie, a list of the frequencies of each word # transform text into a list words-removing punctuation and filtering small words When Lear calls upon the heavens to avenge his cause, 'for they are old like him,' there is nothing extravagant or impious in this sublime identification of his age with theirs for there is no other image which could do justice to the agonising sense of his wrongs and his despair! ''' Things are equal to the imagination, which have the power of affecting the mind with an equal degree of terror, admiration, delight, or love.

text = ''' The intensity of the feeling makes up for the disproportion of the objects. If you had no a priori knowledge of the text's subject matter, a common technique is to infer the importance or weight of a given word/term from its frequency, or importance = 1/frequency. Keywords for a given document are (obviously) chosen from among important words in a document-ie, those words that are likely to distinguish it from another document. I recommend it highly.īelow is a simple template, in python code, for the problem raised in your Question although it's a template it runs-supply any text as a string (as i've done) and it will return a list of word frequencies as well as a ranked list of those words in order of 'importance' (or suitability as keywords) according to a very simple heuristic. It also has an extensive collection of tutorials, FAQs, etc.

KIDS CONTENT TAGGER PROFESSIONAL

This is a stable, mature library created and maintained by professional computational linguists. # return the 5 n-grams with the highest PMIįirst, the key python library for computational linguistics is NLTK (' Natural Language Toolkit').

KIDS CONTENT TAGGER HOW TO

If you want to extract multiword tags, see the StackOverflow question How to extract common / significant phrases from a series of text entries.īorrowing from my answer to that question, the NLTK collocations how-to covers how to doĮxtract interesting multiword expressions using n-gram PMI in a about 7 lines of code, e.g.: import nltkīigram_measures = ()įinder = om_words( To identify the 5 best keywords to associate with a document, you would just sort the terms by their PMI score with the document and pick the 5 with the highest score. This will roughly tell you how much less (or more) surprised you are to come across the term in the specific document as appose to coming across it in the larger collection. To identify keywords like this, you could use the point-wise mutual information of the keyword and the document. This would suggest that Markov might be a good keyword or tag to associate with the document. However, in a particular document from the same collection Markov shows up very frequently. For example, say in a larger collection of documents the term 'Markov' is almost never seen. One way to do this would be to extract words that occur more frequently in a document than you would expect them to by chance.

Kids content tagger