Type of Supervised Text Classification System for Unstructured Text Comments using Probability Theory Technique
Sreedhar Kumar S1, Syed Thouheed Ahmed2, Nisha Bai3, Vınutha B A4
1Sreedhar Kumar S, Department of Computer Science and Engineering, Dr. T Thimmaiah Institute of Technology, VTU, Kolar Golad Field, (Karnataka), India.
2Syed Thouheed Ahmed, Department of Computer Science and Engineering, Dr. T Thimmaiah Institute of Technology, VTU, Kolar Golad Field, (Karnataka), India.
3Nisha Bai, Department of Computer Science and Engineering, Dr. T Thimmaiah Institute of Technology, VTU, Kolar Golad Field, (Karnataka), India.
4Vınutha B A, Department of Computer Science and Engineering, Dr. T Thimmaiah Institute of Technology, VTU, Kolar Golad Field, (Karnataka), India.
Manuscript received on 21 September 2019 | Revised Manuscript received on 06 October 2019 | Manuscript Published on 11 October 2019 | PP: 860-866 | Volume-8 Issue-2S10 September 2019 | Retrieval Number: B11580982S1019/2019©BEIESP | DOI: 10.35940/ijrte.B1158.0982S1019
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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: In this paper, an improved sentimental text analysis system called Probability Based Text Classifier (PBTC) is presented. It aims to train the existing unstructured text command set and to classify the sampled text command belongs into positive or negative polarity based on probability theory and supervised concepts. It consists of three stages pre-processed, training and classification. In the first stage, the proposed (PBTC) system identifies the relevant and irrelevant words in the unstructured text command set based on pre-determined text pattern model. In the second stage it identifies two dissimilar classes over the preprocessed text command set based on predetermined text pattern model and simple probability theory concepts. Next stage, the PBTC identifies the sample test text command without class label belong on which class based on Naive Bayer scheme and trained existing text command set. Experimental result shows that the proposed (PBTC) system is well suitable to train the unstructured text command set and classify the new text command belongs into positive or negative polarity with higher accuracy.
Keywords: Classifier; Probability Based Text Classifier; Unstructured Text Command; Text Pattern; Supervised; Sentimental Analysis.
Scope of the Article: Classification