From the machine learning point of view, raw text is useless. Only if we manage to transform it into meaningful numbers, can we feed it into our machine-learning algorithms such as clustering. The same is true for more mundane operations on text, such as similarity measurement.
One text similarity measure is the Levenshtein distance, which also goes by the name edit distance. Let's say we have two words, "machine" and "mchiene". The similarity between them can be expressed as the minimum set of edits that are necessary to turn one word into the other. In this case, the edit distance would be 2, as we have to add an "a" after "m" and delete the first "e". This algorithm is, however, quite costly, as it is bound by the product of the lengths of the first and second words.
Looking at our posts, we could cheat by treating the whole word as characters and performing the edit distance calculation on the word level. Let's say we have two posts (let...
