1. A method, comprising: generating a plurality of topic clusters, wherein a topic cluster among the plurality of topic clusters comprises a corresponding plurality of topically related words and a corresponding cluster identifier, the generation including: selecting a set of potential seed words from a data set; filtering the set of potential seed words to remove potential seed words within a threshold similarity of each other; forming a set of seed words based at least in part on the filtered set of potential seed words; and for a seed word in the set of seed words, forming a topic cluster comprising topically related words found in the data set, the topically related words being topically related to the seed word; identifying, for a set of non-stop words in a text, a corresponding set of related topic clusters relating to the set of non-stop words, the identification being based at least in part on the plurality of topic clusters; for non-stop words in the set of non-stop words that are identified to have corresponding set of related topic clusters, replacing the non-stop words with corresponding cluster identifiers of the corresponding related topic clusters to generate a clustered version of the text; and providing the clustered version of the text to be further analyzed.
1. A method, comprising: generating a plurality of topic clusters, wherein a topic cluster among the plurality of topic clusters comprises a corresponding plurality of topically related words and a corresponding cluster identifier, the generation including: selecting a set of potential seed words from a data set; filtering the set of potential seed words to remove potential seed words within a threshold similarity of each other; forming a set of seed words based at least in part on the filtered set of potential seed words; and for a seed word in the set of seed words, forming a topic cluster comprising topically related words found in the data set, the topically related words being topically related to the seed word; identifying, for a set of non-stop words in a text, a corresponding set of related topic clusters relating to the set of non-stop words, the identification being based at least in part on the plurality of topic clusters; for non-stop words in the set of non-stop words that are identified to have corresponding set of related topic clusters, replacing the non-stop words with corresponding cluster identifiers of the corresponding related topic clusters to generate a clustered version of the text; and providing the clustered version of the text to be further analyzed. 6. The method of claim 1 , wherein the forming the topic cluster includes applying a word similarity measurement.