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Simulated and self-sustained classification of Twitter Data based on its sentiment

Publication Type : Journal Article

Thematic Areas : Center for Computational Engineering and Networking (CEN)

Publisher : Indian Journal of Science and Technology

Source : Indian Journal of Science and Technology, Indian Society for Education and Environment, Volume 8, Issue 24, Number 24 (2015)

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Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2015

Abstract : We present a methodology for naturally grouping the estimation of Twitter messages. Miniaturized scale websites are a testing new wellspring of data for information mining methods. The aim of this paper is to focus the careful feeling of the information from the microblogging site Twitter. Tweets regularly likewise contain URLs to different sites. Tweets additionally contain a certain measure of OOV (Out-Of-Vocabulary) words, for example, Hash tags, a labeling framework for points permitting Tweets in a comparative vein of discussion to be found. Other OOV words incorporate notice which is a system to direct a Tweet to one or more users. The KH coder tool gives a conventional precision result where the content is POS labeled and MySQL is utilized for putting away points of interest as a part of the database. The R tool is utilized to view the factual examination of information. Further, machine learning calculation has likewise been performed. A preprocessing and highlight choice system in blend with a Maximum Entropy, Naive Bayes and Decision Tree classifiers has been exhibited and sensible results has been delivered. Accuracy of the machine adapting methods for sentiment has been thought about and statistical representation of the classes has been depicted through KH Coder.

Cite this Research Publication : S. N. Vinithra, Selvan, S. J. Arun, M. Kumar, A., and Dr. Soman K. P., “Simulated and self-sustained classification of Twitter Data based on its sentiment”, Indian Journal of Science and Technology, vol. 8, no. 24, 2015.

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