Skip to main navigation menu Skip to main content Skip to site footer
Academia Open

Engineering

Vol 3 (2020): December

Classification of Words of Wisdom in Indonesian on Twitter Using Naïve Bayes and Multinomial Naive Bayes
Klasifikasi Kalimat Mutiara Berbahasa Indonesia Pada Twitter Dengan Menggunakan Naïve Bayes dan Multinomial Naive Bayes



(*) Corresponding Author
DOI
https://doi.org/10.21070/acopen.3.2020.787
Published
October 15, 2020

Abstract

Quote is a sentence made with the hope that someone becomes a strong personality, an individual who always improves himself to advance and achieve success. Social media is a place for people to express their hearts to the world which is sometimes a heart expression in the form of quotes. The purpose of this study is to classify Indonesian quotes on Twitter using Naïve Bayes and Multinomial Naïve Bayes. This experiment uses text classification from Twitter data written by Twitter users whether the quotes are then classified again into 6 categories (Love, Life, Motivation, Education, Religion, Others). The language used is Indonesian. The methods used are Naive Bayes and Multinomial Naïve Bayes. Results of this experiment is a classified Indonesian quote collection web application. This classification makes it easy for users to search for quotes based on class or keyword. For example, when a user wants to search for 'motivational' quotes, this classification can be very useful.

References

  1. Akbarisanto Ridho, Danar Wikan, Purwarianti Ayu. Analyzing Bandung Public Mood Using Twitter Data. School of Electrical Engineering and Informatics, Bandung, Indonesia, 2016 https://doi.org/10.1109/icoict.2016.7571910
  2. Chris Tseng, Nishant Pateli, Hrishikesh Paranjape, T Y Lin,SooTee Teoh. Classifying Twitter Data with Naive Bayes Classifier. Computer Science Dept., San Jose State University, 2012.
  3. Raveena Dayani, Nikita Chhabra, Taruna Kadian and Rishabh Kaushal. Rumor Detection in Twitter: An Analysis in Retrospect. Department of Information Technology Indira Gandhi Delhi Technical University for Women, Delhi, India, 2009. https://doi.org/10.26634/jes.6.2.14757
  4. Sari Widya Sihwi, Insan Prasetya Jati, Rini Anggrainingsih. Twitter Sentiment Analysis of Movie Reviews Using Information Gain and Naïve Bayes Classifier. Universitas Sebelas Maret Surakarta, Indonesia. 2018. https://doi.org/10.1109/isemantic.2018.8549757
  5. J. Han, M. Kamber, dan J. Pei, Penambangan Data: Konsep dan Teknik ,Edit Ketiga., Vol. 3. Morgan Kaufmann, 2012.
  6. TwitterStreamingAPI.http://apiwiki.twitter.com/Streaming-API-Documentation.AccessedFebruary1,2010.
  7. Apache Lucene. http://lucene.apache.org. Accessed February1, 2010.C. D. Manning, P. Raghavan, and H. Schutze.Introdution toInformation Retrieval, pages 108–115. Cambridge UniversityPress, New York, 2008.
  8. J.R. Finkel, T. Grenager, and C. Manning.IncorporatingNon-local Information into Information Extraction Systems byGibbs Sampling.Proceedings of the 43nd Annual Meetingof the Association for Computational Linguistics (ACL 2005),pages 363–370, 2005. https://doi.org/10.3115/1219840.1219885
  9. V. Qazvinian, E. Rosengren, Dragomir R. Radev, Q. Mei.Rumor hasit: Identifying Misinformation in Microblogs. In Proceedings of the2011 Conference on Empirical Methods in Natural Language Processing(2011).
  10. Goncalves Eduardo Correa, NBBR: A Baseline Method for the Evaluation of Bayesian Multi-label Classification Algorithms. Universidade Federal Fluminense (UFF). Niteroi, Rio de Janeiro 24210–240, 2014. https://doi.org/10.1109/iccsa.2014.56

Downloads

Download data is not yet available.