Automatic Tagging Algorithm

Does anyone know how to bui开发者_开发知识库ld automatic tagging (blog post/document) algorithm? Any example will be appreciated.

I agree with what Wooble is saying. However the naïve solution is to simply write an algorithm that calculates the lexical similarities and differences of the given blog post compared to a corpus of text. This lexical difference will give you words that are found in the blog post with more frequency than those found in the corpus. And from those words, you can infer a tag.

But I strongly recommend against it. Automatic tagging doesn't seem to work in practice. Just outsource the tagging work to your users or to services like Mechanical Turk

Late response but also had this task for a course - so in case someone else is looking to explore this, here is a starting point:

If you are looking for simple solutions or perhaps as a machine learning exercise, you might view automatic tagging as a text categorization/classification task. Naive Bayes classifiers are simple tools to figure out and there is plenty of pseudocode and material to understand these. TFIDF (term frequency-inverse document frequency) metric is something else you can look into - although commonly associated with information retrieval it can be tasked for this problem when combined with other machine learning techniques.

However, instead of assigning the new sample a single label based on a the definition of NB classifier, you will have to determine multiple labels. You can probably use the tag co-occurrence information from training set to help you with this.

This is a simplistic and naive solution and there are a lot of details on feature selection left out (stemming to reduce independent parameters, information gain, etc). Plenty of easily accessible papers on this research topic to try it out!