We are the creators of uClassify. We really enjoy to use our own web service and intend to share as many classifiers as possible!
This classifier determines if a text is positive or negative. It is well suited for both short and long texts (tweets, Facebook statuses, blog posts, product reviews etc). It’s trained on 2.8 million documents with data from Twitter, Amazon product reviews and movie reviews. It can be used to conduct research, brand surveys and see trends around market campaigns. You may access the sentiment analysis api by signing up (free)! Read more about the classifier in at blog.uclassify.com/sentiment-analysis-api
by uClassifyThe IAB Taxonomy V2 classifier categories texts into 560 topics. It has four levels of depth, a main category (e.g. sports, business or science) and subcategories (soccer, agriculture or physics). It's based on the IAB Quality Assurance Guidelines (QAG) Content Taxonomy V2, released 1 March 2017. The class name has up to 6 parts, separated by underscores: "main topic_sub topic_id1_id2_id3_id4" where the ids are the IAB level ids (use the ids for mapping).
by uClassifyCategories a text into a topic (Arts, Business, Computers, Games, Health, Home, Recreation, Science, Society and Sports). Each of those topics has more specific child classifier (Art Topics, Business Topics etc). It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyThis classifier tries to figure out if a text is written by a male or female. It has been trained on 11000 blogs (5500 blogs written by females and 5500 by males). More text gives better results.
by uClassify
We recommend using our new better classifier called 'Language Detector' instead. Classifies the language of a text by looking on about 4000 commonly used words per language. It works best with clean texts but can also be used for HTML pages. For reliable results HTML pages need more text content (since HTML often contains English words and comments).
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The IAB Taxonomy classifier categories texts into one of 360 topics. It has two levels of depth, a main category (e.g. sports, business or science) and subcategories (soccer, agriculture or physics). It's based on the IAB Quality Assurance Guidelines (QAG) Taxonomy. The class name has 4 parts, separated by underscores: "main topic_sub topic_id1_id2" where the ids are the IAB level 1 and 2 ids.
by uClassifyThis classifier tries to estimate to which age group a blog belongs. The training data is based upon about 7000 blogs collected randomly from Internet.
by uClassifyThe IAB Content Taxonomy V3 classifier categorizes text into 617 topics. It has a main category (e.g. sports, business or science) and a leaf category (soccer, agriculture or physics). The class name has 3 parts, separated by underscores: "main topic_sub topic_uid" where the uid is the category's official IAB unique id. This text classifier is based on the IAB Quality Assurance Guidelines (QAG) Content Taxonomy V3, released in September 2021.
by uClassifyCategories a text into a computer topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into a society topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into a sport topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into an art topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into an business topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
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This classifier identifies the language of a text. It can detect more than 370 major and rare languages; living (e.g. English, Chinese), constructed (e.g. Klingon, Esperanto), ancient and extinct. At least a few words are needed to get accurate results. The language name is appended with an underscore followed by its ISO 639-3 code. The text is expected to be UTF-8 encoded, also keep in mind that more languages may be added.
by uClassifyCategories a text into a recreation topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into a home topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into a health topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into a science topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into a game topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassify
This classifier has been trained on 21 different classical authors. We have used about three books per author collected from the Gutenberg project. It only works for English texts. Try it out to see which poet your blog or text is most alike!
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Discourse denotes written and spoken communications, it can classify phrases into questions, answers or more fine grained categories such as agreement, disagreement, elaboration etc. It works best with short texts such as tweets. For longer texts consider splitting it into sentences or phrases on beforehand. It's based on the dataset from the paper "Characterizing Online Discussion Using Coarse Discourse Sequences (ICWSM '17)"
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This news categorizer is only a simple example used in a tutorial. It has been trained on 20 texts per category (sports, entertainment and science) so don't expect too much (even though it seems to do incredibly well).
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This classifier will try to predict if a movie passes or fails the Bechdel Test by looking at the plot and/or subtitles of a movie. It's is trained with about 6000 IMDB plots and 2400 movie subtitles that has failed and passed the Bechdel Test according to bechdeltest.com.
by uClassify