Supervised text classification algorithms require a large number of documents labeled by humans, that involve a labor-intensive and time consuming process.

We evaluate this approach to improve performance of text classification on three real world datasets.

In supervised text classification learning algorithms, the learner (a program) takes human labeled documents as input and learns a decision function that can classify a previously unseen document to one of the predefined classes.

In this paper, we propose a text classification algorithm based on Latent Dirichlet Allocation (LDA) (Blei et al., 2003) which does not need labeled documents.

(Blei et al., 2003) used LDA topics as features in text classification , but they use labeled documents while learning a classifier.

Several researchers have proposed semi-supervised text classification algorithms with the aim of reducing the time, effort and cost involved in labeling documents.

Semi-supervised text classification algorithms proposed in (Nigam et al., 2000), (J oachims, 1999), (Zhu and Ghahra—mani, 2002) and (Blum and Mitchell, 1998) are a few examples of this type.

Also a human annotator may discard or mislabel a polysemous word, which may affect the performance of a text classifier .

The H-groups shown in Table 1 provide richer semantic descriptions of the domain than keywords do, and we noted potential applications for high-level summarization of a whole corpus, the creation of information extraction templates and finer- grained text classification and retrieval.

For broad topics it is desirable to perform f1ner- grained text classification and retrieval.

The alternation in V-groups contained by H-groups may reflect different beliefs and opinions which could be used for text classification and opinion mining.