# guide for text classification using weka

I have a set of 2000 small texts (each less than 500 words) that I manually categorized. All the texts are in the same main subject, and I want to separate them into distinct groups based on their similarity and focus on the topic. I would like to know what would be the best approach to automatically separate these texts. I do not have a training set and I would like to confirm the existing labeling or find an alternative clustering of my dataset.

• Voting to leave open, this seems a straightforward question about topic modeling, which is squarely on-topic. (A little broad, perhaps.) – Sean Easter Aug 23 '17 at 15:16

In the Weka Explorer you can save the cluster assignment by right-clicking the result in the result list, choose Visualize cluster assignments, then click Save.
To compare the clustering result with the existing categorisation, assuming the dataset does contain the existing class, select Classes to clusters evaluation and choose the class attribute from the dropdown.