Open AccessAnalysis of Text Classification with various Term Weighting Schemes in Vector Space Model
Author(s) -
Shekhar Jain,
Shekhar Jain,
Santosh K. Vishwakarma
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d1938.0891020
Subject(s) - weighting , term (time) , support vector machine , computer science , naive bayes classifier , artificial intelligence , context (archaeology) , matching (statistics) , vector space model , pattern recognition (psychology) , data mining , machine learning , mathematics , statistics , geography , medicine , physics , archaeology , quantum mechanics , radiology
Term Weighting Scheme (TWS) is a key component of the matching mechanism when using the vector space model In the context of information retrieval (IR) from text documents, the this paper described a new approach of term weighting methods to improve the classification performance. In this study, we propose an effective term weighting scheme, which gives highest accuracy with compare to the text classification methods. We compared performance parameter of KNN and Naïve Bayes Classification with different Weighting Method, Weight information gain, SVM and proposed method.We have implemented many term-weighting methods (TWM) on Amazon data collections in combination with Information-Gain and SVM and KNN algorithm and Naïve Bayes Algorithm.