<p>How does one automatically find categories for text based on content?</p>

[{'answer_id': 65500, 'author': 'GEOCHET', 'author_id': 5640, 'author_profile': 'https://Stackoverflow.com/users/5640', 'pm_score': 0, 'selected': False, 'text': '<p>There is a good paper written on this: <a href="http://www.cs.utexas.edu/users/hyukcho/classificationAlgorithm.html" rel="nofollow noreferrer">http://www.cs.utexas.edu/users/hyukcho/classificationAlgorithm.html</a></p>\n'}, {'answer_id': 65648, 'author': 'Jahangir', 'author_id': 6927, 'author_profile': 'https://Stackoverflow.com/users/6927', 'pm_score': 0, 'selected': False, 'text': '<p>The best way to categorize content, be it text or multimedia is to use a <a href="http://en.wikipedia.org/wiki/Enterprise_taxonomy" rel="nofollow noreferrer">taxonomy</a>.\nMost of the well known CMSs have built in support for Taxonomy. <a href="http://drupal.org" rel="nofollow noreferrer">Drupal</a> has one of the best support for <a href="http://drupal.org/handbook/modules/taxonomy" rel="nofollow noreferrer">taxonomy</a> among the various CMSs out there.</p>\n'}, {'answer_id': 403683, 'author': 'Roberto Russo', 'author_id': 49716, 'author_profile': 'https://Stackoverflow.com/users/49716', 'pm_score': 2, 'selected': False, 'text': '<ol>\n<li>Read <strong><a href="http://www.cs.waikato.ac.nz/~ml/weka/book.html" rel="nofollow noreferrer">Data Mining: Practical Machine Learning Tools and Techniques</a></strong> - Ian H. Witten,  Eibe Frank</li>\n<li>Use <strong><a href="http://www.cs.waikato.ac.nz/~ml/weka/" rel="nofollow noreferrer">Weka</a></strong> or <strong><a href="http://www.ailab.si/orange/" rel="nofollow noreferrer">Orange</a></strong></li>\n</ol>\n'}, {'answer_id': 1068968, 'author': 'theycallmemorty', 'author_id': 75694, 'author_profile': 'https://Stackoverflow.com/users/75694', 'pm_score': 2, 'selected': False, 'text': '<p>I would encourage you to look at the text classification libraries bundled with the <a href="http://www.nltk.org/" rel="nofollow noreferrer">Natural Language Toolkit</a>.  Even if you\'re not familiar with Python I think you\'ll find the API rather intuitive.  There are many good examples in the <a href="http://www.nltk.org/book" rel="nofollow noreferrer">NLTK Book</a> and the people on the mailing list are quite helpful as well.</p>\n'}, {'answer_id': 23315191, 'author': 'Annamalai N', 'author_id': 2484501, 'author_profile': 'https://Stackoverflow.com/users/2484501', 'pm_score': 1, 'selected': False, 'text': '<p>Simplest way to do text categorization is to use <a href="http://en.wikipedia.org/wiki/Bag-of-words_model" rel="nofollow">bag-of-words</a> representation. Words/ n-grams of words in each document could be used as features. With this you can represent every document as vector in metric space. Subsequently, you can apply <a href="http://en.wikipedia.org/wiki/Cluster_analysis" rel="nofollow">clustering</a> to group documents that are similar in terms of content. For instance, you may use k-means clustering with these vectors to cluster lexically similar documents together.</p>\n\n<p>Python based text mining workbench, <a href="http://www.nltk.org/" rel="nofollow">NTLK</a> is excellent for experimenting tasks like these quickly (in general, python is pretty good for working with text). You may find it useful. </p>\n'}]
['https://Stackoverflow.com/questions/65487', 'https://Stackoverflow.com', 'https://Stackoverflow.com/users/-1/']