An Innovative Model for Relevance Feature Discovery for Text Mining

Author(s):

Suvarna Jadhav , Late G.N.Sapkal College of Engg. Nashik,India; Ms. Suvarna Jadhav, Late G.N.Sapkal College of Engg. Nashik,India; Prof. N. R. Wankhade, Late G.N.Sapkal College of Engg. Nashik,India

Keywords:

Text Mining, Text Features Extraction, Text Classification, Weight Calculation, Noise Removal

Abstract:

Many methods are used for discovery of relevance features but in all previous process suffered from some advantages and disadvantages. Describing user preferences is a big challenge to guarantee the quality of discovered relevance features in text documents, because of large scale terms and data patterns. Term-based approaches, most existing popular text mining and classification methods have adopted. They have all suffered from the problems of polysemy and synonymy. Yet, how to effectively use large scale patterns remains a major problem in text mining, over the years, It is assumed t that pattern-based methods should perform better than term-based ones. This paper presents an innovative model for relevance feature discovery. Higher level features are deployed over low-level features (terms), it discovers both positive and negative patterns in text documents. On their specificity and their distributions in patterns, it also classifies terms into categories and updates term weights.

Other Details:

Manuscript Id	:	IJSTEV3I6126
Published in	:	Volume : 3, Issue : 6
Publication Date	:	01/01/2017
Page(s)	:	169-172

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