throwing away words that occur so seldom that there is only small chance that they occur in future posts counting the remaining words calculating tf-idf values from the countsconsidering the whole text corpus again we can congratulate ourselves with this processwe are able to convert bunch of noisy text into concise representation of feature values butas simple and as powerful as the bag-of-words approach with its extensions isit has some drawbacks that we should be aware of they are as followsit does not cover word relations with the previous vectorization approachthe text "car hits walland "wall hits carwill both have the same feature vector it does not capture negations correctly for instancethe text " will eat ice creamand " will not eat ice creamwill look very similar by means of their feature vectorsalthough they contain quite the opposite meaning this problemhowevercan be easily changed by not only counting individual wordsalso called unigramsbut also considering bigrams (pairs of wordsor trigrams (three words in rowit totally fails with misspelled words although it is clear to the readers that "databaseand "databasconvey the same meaningour approach will treat them as totally different words for brevity' sakelet us nevertheless stick with the current approachwhich we can now use to efficiently build clusters from clustering finallywe have our vectors that we believe capture the posts to sufficient degree not surprisinglythere are many ways to group them together most clustering algorithms fall into one of the two methodsflat and hierarchical clustering