research-article

Comparing SVM and KNN Algorithms for Myanmar News Sentiment Analysis System

Authors:

Khin Thandar NwetAuthors Info & Claims

ICCDE '20: Proceedings of 2020 6th International Conference on Computing and Data Engineering

Pages 65 - 69

https://doi.org/10.1145/3379247.3379293

Published: 07 March 2020 Publication History

Abstract

Sentiment analysis is one of the natural language processing research fields that identify the polarity and subjectivity of documents. With the increasing of web technology, large volumes of data are available from many resources. Sentiment analysis applications are widely applied in many domains from news, financial, election, blogs, and post. News is very important and provides valuable information for society. News is tagged as positive and negative in this system. News is collected from many websites and ALT tree bank. N-gram and TF-IDF feature extraction and selection methods are used in this system to get more performance. Supervised machine learning algorithm is a classification algorithm that uses labeled data. K nearest neighbors is a simple algorithm that stores all available features and classifies new features based on a similarity measure. SVM is one of the classifier that has higher accuracy. This paper shows comparison of performance results for sentiment analysis system by using support vector machine (SVM) and K nearest neighbor (KNN) algorithms.

References

[1]

Liu, B. 2012. "Sentiment Analysis and Opinion Mining", Synthesis Lecturer on Human Language Technologies, Morgan & Claypool, New York, United State.

[2]

Lilleberg, J., Zhu, Y., and Yanqing, Z. 2015. "Support Vector Machines and Word2vec for Text Classification with Semantic Features", 14th International Conference. on Cognitive Inlormatics & Cognitive Computing, IEEE

[3]

Doddi1, K.S., Haribhakta, Y. V., and Kulkarni. P., 2014. "Sentiment Classification of News Articles", International Journal of Computer Science and Information Technologies, Vol. 5 (3), 4621--4623

[4]

Tyagi, E. and Sharma, A. K. 2017. "Sentiment Analysis of Product Reviews using Support Vector Machine Learning Algorithm", Indian Journal of Science and Technology, Vol 10(35).

[5]

Jurafsky, D., and Martin, J.H. 2014. "N-Grams," chapter 4, Speech and Language Processing, 2014.

[6]

Pa, W.P., and Thein, N. 2009. "Myanmar Word Segmentation Using a Combined Model", Ecase 2019.

[7]

Robertson, S. 2004. "Understanding inverse document frequency: on theoretical arguments for IDF", Journal of Documentation, 60(50), 503--520., https://doi.org/10.1108/00220410410560582

[8]

Smola, A., and Vishwanathan S.V.N. 2011. "Taste of Machine Learning", Introduction to Machine Learning, Cambridge University Press, Cambridge, United Kingdom

[9]

Imandoust, S.B., And Bolandraftar, M. 2013. "Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background", Journal of Engineering Research and Applications, 3(5), 605--610

[10]

Gupta1, A., Pruthi, J., and Sahu, N. 2017. "Sentiment Analysis of Tweets using Machine Learning Approach", International Journal of Computer Science and Mobile Computing, 6(4), 444--458

[11]

Meneses, J. S., Chavez, Z.R., and Rodriguez, J. G. 2019. "Compressed kNN: K-Nearest Neighbors with Data Compression", Entropy, MDPI, Switzerland

[12]

Wang, L. (ed) 2005. "Support Vector Machines: Theory and Applications", Springer, Berlin

[13]

Hastie, T., Tibshirani, R., and Friedman, J. 2017. "Support Vector Machines and Flexible Discriminants", The Elements of Statistical Learning, Springer, California, United States

[14]

Laura A. and Rouslan A. M. 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis", DIW Berlin

[15]

Kaushik H., 2017."Sentiment Analysis using Ensemble Classifier", International Journal of Innovative Research in Computer and Communication Engineering, 5( 9), 15125--15130

Cited By

Mitreva EGeorgiev VNikolova A(2024)Classification of Short Noisy TextProceedings of the International Conference on Computer Systems and Technologies 202410.1145/3674912.3674935(227-231)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3674912.3674935
Khine WZin T(2024)Neural Sentiment Network Model for Myanmar Language Using Attention Mechanism (AttenSentNet)2024 5th International Conference on Advanced Information Technologies (ICAIT)10.1109/ICAIT65209.2024.10754915(1-6)Online publication date: 7-Nov-2024
https://doi.org/10.1109/ICAIT65209.2024.10754915
Rathi BMadeira F(2023)Early Prediction of Diabetes Using Machine Learning Techniques2023 Global Conference on Wireless and Optical Technologies (GCWOT)10.1109/GCWOT57803.2023.10064682(1-7)Online publication date: 24-Jan-2023
https://doi.org/10.1109/GCWOT57803.2023.10064682
Show More Cited By

Index Terms

Comparing SVM and KNN Algorithms for Myanmar News Sentiment Analysis System
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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ICCDE '20: Proceedings of 2020 6th International Conference on Computing and Data Engineering

January 2020

279 pages

ISBN:9781450376730

DOI:10.1145/3379247

Copyright © 2020 ACM.

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Published: 07 March 2020

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ICCDE 2020: 2020 The 6th International Conference on Computing and Data Engineering

January 4 - 6, 2020

Sanya, China

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Cited By

Mitreva EGeorgiev VNikolova A(2024)Classification of Short Noisy TextProceedings of the International Conference on Computer Systems and Technologies 202410.1145/3674912.3674935(227-231)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3674912.3674935
Khine WZin T(2024)Neural Sentiment Network Model for Myanmar Language Using Attention Mechanism (AttenSentNet)2024 5th International Conference on Advanced Information Technologies (ICAIT)10.1109/ICAIT65209.2024.10754915(1-6)Online publication date: 7-Nov-2024
https://doi.org/10.1109/ICAIT65209.2024.10754915
Rathi BMadeira F(2023)Early Prediction of Diabetes Using Machine Learning Techniques2023 Global Conference on Wireless and Optical Technologies (GCWOT)10.1109/GCWOT57803.2023.10064682(1-7)Online publication date: 24-Jan-2023
https://doi.org/10.1109/GCWOT57803.2023.10064682
Zhang WChen SLi CLiu GJing D(2023)Sentiment Analysis Method Based on Improved Feature Vector2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT)10.1109/ACAIT60137.2023.10528512(1332-1338)Online publication date: 10-Nov-2023
https://doi.org/10.1109/ACAIT60137.2023.10528512

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