Concept Based Labeling of Text Documents Using Support Vector Machine

Classification plays a vital role in many information management and retrieval tasks. Text classification
uses labeled training data to learn the classification system and then automatically classifies the remaining text using
the learned system. Classification follows various techniques such as text processing, feature extraction, feature
vector construction and final classification. The proposed mining model consists of sentence-based concept analysis,
document-based concept analysis, corpus-based concept-analysis, and concept-based similarity measure. The
proposed model can efficiently find significant matching concepts between documents, according to the semantics
of their sentences. The similarity between documents is calculated based on a similarity measure. Then we analyze
the term that contributes to the sentence semantics on the sentence, document, and corpus levels rather than the
traditional analysis of the document only. With the extracted feature vector for each new document, Support Vector
Machine (SVM) algorithm is applied for document classification. The approach enhances the text classification
accuracy