Analysis of Convolutional Neural Network for Lifelong Learning on Indonesian Sentiment Analysis
Pages 64 - 69
Abstract
Sentiment analysis is a process to obtain the tendency of the authors in an article. Sentiment analysis classifies textual data into a class of positive, negative, or neutral sentiments. CNN is one of the deep learning algorithms capable of classifying textual data into positive, negative, or natural classes. In general, the standard learning methods learn from one domain to produce a model. Another learning paradigm is lifelong learning which is believed to be able to accumulate learning from various domains for learning in the new domain. In this paper, we examine lifelong learning of CNN for sentiment analysis on Indonesian textual data. Our simulation shows that the accuracy of CNN increases with the increase in the number of source domains where CNN learns. This shows that lifelong learning using CNN works well for sentiment analysis on Indonesian textual data.
References
[1]
Pang, B., Lee, L., and Vaithyanathan, S. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (Philadelphia, USA, July, 2002). EMNLP. ACL, 79--86. DOI= http://dx.doi.org/10.3115/1118693.1118704.
[2]
Vanderplas, J. 2016. Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media, Inc.
[3]
LeCun, Y., Bengio, Y., and Hinton, G. 2015. Deep Learning. AK Peters Ltd., Natick, MA.
[4]
Kim, Y. 2014. Convolution neural networks for sentence classification. In Proceedings of Conference on Empirical Methods in Natural Language Processing (Doha, Qatar, October 25-29, 2014). EMNLP. ACL, 1746--1751. DOI= http://dx.doi.org/10.3115/v1/D14-1181.
[5]
Zhang, Y., and Wallace, B. C. 2016. A sensitivity analysis of (and practitioners' guide to) convolution neural networks for sentence classification. In Proceedings of the eight International Joint Conference on Natural Language Processing (Taipei, Taiwan, November 27 --December 1, 2017). 253--263.
[6]
Chen, Z., and Liu, B. 2016. Lifelong Machine Learning. Morgan & Claypool Publisher.
[7]
Silver, D. L., Yang, Q., and Li, L. 2018. Lifelong Machine Learning System: Beyond Learning Algorithms. Presented at the Conference: AAAI 2013 Spring Symposium on Learning Machine Learning, (Weihai, China, October 27-29, 2018).
[8]
Saputr, O., Murfi, H., and Nurrohmah, S. 2018. Analysis of Convolution Neural Network for Transfer Learning of Sentiment Analysis in Indonesian Tweets. In Proceedings of the International Conference on Data Science and Information Technology, (Singapore, July, 2018). DSIT. ACM, New York, NY, 18--22. DOI= https://doi.org/10.1145/3239283.3239299.
[9]
Chen, Z., Ma, N., and Liu, B. 2015. Lifelong Learning for Sentiment Classification. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Beijing, China, July, 2015). ACL | IJCNLP. 750--756. DOI= http://dx.doi.org/10.3115/v1/P15-2123.
Index Terms
- Analysis of Convolutional Neural Network for Lifelong Learning on Indonesian Sentiment Analysis
Recommendations
Analysis of convolution neural network for transfer learning of sentiment analysis in Indonesian tweets
DSIT '18: Proceedings of the 2018 International Conference on Data Science and Information TechnologySentiment analysis is an activity to classify public opinion about entities in textual data into positive or negative. One of the automatic methods for sentiment analysis is convolution neural network (CNN). CNN consists of many layers with many ...
A Word-Character Convolutional Neural Network for Language-Agnostic Twitter Sentiment Analysis
ADCS '17: Proceedings of the 22nd Australasian Document Computing SymposiumConvolutional Neural Networks (CNN) have been widely used for text classification. Both word-based CNNs and character-based CNNs have shown good performance for Twitter sentiment classification. Most research on CNNs is towards English Twitter sentiment ...
Sentiment Analysis of Network Comments Based on GCNN
CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial IntelligenceRecently, satisfactory performance has been achieved on sentiment analysis tasks by using recurrent neural network (RNN). As a derived model of RNN, gated recurrent units (GRU) model has a great advantage in dealing with long text sequence problems, ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
January 2020
117 pages
ISBN:9781450377348
DOI:10.1145/3424311
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Auckland University of Technology
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 November 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICICSE '20
ICICSE '20: 2020 International Conference on Internet Computing for Science and Engineering
January 14 - 16, 2020
Male, Maldives
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 54Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in