A Novel Cosine-based Internal and External Validation metrics to assess twitter Data Clustering using Hybrid Topic Models

Abstract

In document clustering labeled and unlabeled documents are organized into a desired number of coherent and meaningful sub-clusters. Topic models are useful in extracting cluster tendency from Twitter-based data document clusters. Evaluating cluster tendency and performance with a reliable metric is one of the unsolved problems in topic document clustering. In the previous study cluster validity metrics have been proposed under Euclidean distance measure, these metrics underperform in topic models when dealing with numerical databases and for large corpus datasets. In this paper, to assess Twitter data clustering a novel cosine based internal and external validity indices used by considering closeness between documents, the lexical similarity and cluster classification metrics for each topic separately using Hybrid topic models. Experimentally proved the effectiveness of cosine based internal and external validity metrics and results compared with Euclidean metrics using benchmark and Twitter-based datasets.