The area of multi label text classification is getting more attention of researchers because of its role in the field of information retrieval , text mining , web mining etc. Supervised methods from machine learning are mainly used for its realization. But as it needs labeled data for classification all the time , semi supervised methods are now a day getting popular in the MLTC domain. The goal of Semi supervised learning is to reduce the classification errors using readily available unlabeled

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... data in conjunction with available labeled data. This paper mainly provides survey and analysis of various semi supervised methods used in multi label text classification task ; This overview concludes that consideration of semantic aspects of input document datasets , their representation in conjunction with smoothness and manifold assumptions in semi supervised learning may give more relevant classification results.