Sentence Sentiment Classification Using Convolutional Neural Network in Myanmar Texts
Abstract
There are still few works on application of deep learning for Myanmar language. This paper presents an approach to use a convolutional neural network (CNN) model to classify sentence sentiment in Myanmar texts. A CNN model is constructed on the top of a word embedding model (i.e., Word2Vec), which converts words into vectors. The model classifies the input sentences and labels each sentence with positive, negative, neutral, unrelated and unreadable sentiments. The model is learnt from 1,152 sentences taken from the customers' reviews of products, which is provided by a telecommunication company. Then, the model is tested on 495 unseen sentences with the result of 86.26% accuracy and 82.58% average f-measure in prediction. The model is compared with the traditional machine learning (ML) classifiers, especially support vector machine (SVM), naïve Bayes (NB), and logistic regression (LR). The model outperforms these classifiers since SVM results in 64.44% accuracy, NB obtains 60.20% accuracy and LR gets 55.15% accuracy.
References
[1]
Mukherjee, S. and Bhattacharyya, P., 2013. Sentiment analysis: A literature survey. arXiv preprint arXiv:1304.4520.
[2]
Aydoğan, E. and Akcayol, M.A., 2016, August. A comprehensive survey for sentiment analysis tasks using machine learning techniques. In 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1--7). IEEE.
[3]
Zhang, L., Wang, S. and Liu, B., 2018. Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), p. e1253.
[4]
Ain, Q.T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B. and Rehman, A., 2017. Sentiment analysis using deep learning techniques: a review. Int J Adv Comput Sci Appl, 8(6), p. 424.
[5]
Wu, J., 2017. Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University. China, 5, p. 23.
[6]
Aye, Y.M. and Aung, S.S., 2018. Senti-Lexicon and Analysis for Restaurant Reviews of Myanmar Text. International Journal of Advanced Engineering, Management and Science, 4(5).
[7]
Severyn, A. and Moschitti, A., 2015, August. Twitter sentiment analysis with deep convolutional neural networks. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 959--962). ACM.
[8]
Dos Santos, C. and Gatti, M., 2014, August. Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (pp. 69--78).
[9]
Vateekul, P. and Koomsubha, T., 2016, July. A study of sentiment analysis using deep learning techniques on Thai Twitter data. In 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 1--6). IEEE.
[10]
Pasupa, K. and Ayutthaya, T.S.N., 2019. Thai Sentiment Analysis with Deep Learning Techniques: A Comparative Study based on Word Embedding, POS-tag, and Sentic Features. Sustainable Cities and Society.
[11]
Kim, Y., 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
[12]
Kim, H. and Jeong, Y.S., 2019. Sentiment Classification Using Convolutional Neural Networks. Applied Sciences, 9(11), p. 2347.
[13]
Deriu, J.M. and Cieliebak, M., 2016. Sentiment analysis using convolutional neural networks with multi-task training and distant supervision on italian tweets. In Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, Napoli, Italy, December 5-7, 2016. Italian Journal of Computational Linguistics.
[14]
Santos, I., Nedjah, N. and de Macedo Mourelle, L., 2017, November. Sentiment analysis using convolutional neural network with fastText embeddings. In 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1--5). IEEE.
[15]
Nedjah, N., Santos, I. and de Macedo Mourelle, L., 2019. Sentiment analysis using convolutional neural network via word embeddings. Evolutionary Intelligence, pp. 1--25.
Index Terms
- Sentence Sentiment Classification Using Convolutional Neural Network in Myanmar Texts
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Published In
March 2020
168 pages
ISBN:9781450376952
DOI:10.1145/3388818
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In-Cooperation
- Nanyang Technological University
- The Hong Kong Polytechnic: The Hong Kong Polytechnic University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 May 2020
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IVSP '20
IVSP '20: 2020 2nd International Conference on Image, Video and Signal Processing
March 20 - 22, 2020
Singapore, Singapore
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