Analysis of Text Classification with various Term Weighting Schemes in Vector Space Model
Shitanshu Jain1, S. C. Jain2, Santosh Vishwakarma3
1Shitanshu Jain, PhD Scholar, Amity University, Noida, (U.P), India.
2Dr. S. C. Jain, Director, ASET, Amity University, Noida, (U.P), India.
3Dr. Santosh K. Vishwakarma, Associate Professor, Department of CSE, Manipal University Jaipur, India.
Manuscript received on July 24, 2020. | Revised Manuscript received on August 02, 2020. | Manuscript published on August 10, 2020. | PP: 390-393 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.D1938029420 | DOI: 10.35940/ijitee.D1938.0891020
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Text Mining, Text Classification, Term Weighting, KNN, Naïve Bayes, SVM.
Scope of the Article: Classification