Welcome To WUJNS
武汉大学学报 英文版 | Wuhan University Journal of Natural Sciences
Wan Fang
CNKI
CSCD
Wuhan University
Latest Article
Sentiment Analysis of Code-Mixed Bambara-French Social Media Text Using Deep Learning Techniques
Time:2018-5-25  
Arouna KONATE, DU Ruiying
1. State Key Laboratory of Software Engineering/School of Computer, Wuhan University, Wuhan 430072, Hubei, China; 2. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, Hubei, China
Abstract:
The global growth of the Internet and the rapid expansion of social networks such as Facebook make multilingual sentiment analysis of social media content very necessary. This paper performs the first sentiment analysis on code-mixed Bam-bara-French Facebook comments. We develop four Long Short-term Memory (LSTM)-based models and two Convolutional Neural Network (CNN)-based models, and use these six models, Naïve Bayes, and Support Vector Machines (SVM) to conduct experiments on a constituted dataset. Social media text written in Bambara is scarce. To mitigate this weakness, this paper uses dictionaries of character and word indexes to produce character and word embedding in place of pre-trained word vectors. We investigate the effect of comment length on the models and perform a comparison among them. The best performing model is a one-layer CNN deep learning model with an accuracy of 83.23 %.
Key words:sentiment analysis; code-mixed Bambara-French Facebook comments; deep learning; Long Short-Term Memory(LSTM); Convolutional Neural Network (CNN)
CLC number:TP 391.1
References:
[1]	Muysken P. Code-Switching and Grammatical Theory[M]. Cambridge: CUP, 1995.
[2]	Gafaranga J, Torras M C. Interactional otherness: Towards a redefinition of code switching[J]. International Journal of Bilingualism, 2002 6(1): 1-22.
[3]	Xiao Z, Liang P. Chinese Sentiment Analysis Using Bidirectional LSTM with Word Embedding[M]. Berlin Heidelberg: Springer-Verlag, 2016.
[4]	Vydrin V. Bamana reference corpus (BRC)[J]. Procedia-Soc Behav Sci, 2013, 95(4): 75-80.
[5]	Balahur A, Steinberger R, Kabadjov M. Sentiment analysis in the news[J]. Infrared Phys Technol, 2014, 65: 94-102.
[6]	Long J, Yu M, Zhou M, et al. Target-dependent Twitter sentiment classification[C]// Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Oregon: Association for Computational Linguistics, 2011: 151-160.
[7]	Vaibhavi N P, Sheikh I R. Twitter as a corpus for sentiment analysis and opinion mining [J]. International Journal of Advanced Research in Computer and Communication Engineering, 2016, 5(12):320-322.
[8]	Go A, Bhayani R, Huang L. Twitter Sentiment Classification Using Distant Supervision[EB/OL]. [2018-03-14]. https:// www.researchgate.net/publication/228523135_ Twitter_ sen- timent_classification_using_distant_supervision.
[9]		Pang B, Lee L, Vaithyanathan S. Thumbs up ? Sentiment classification using machine learning techniques[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Philadelphia: Association for Computational Linguistics, 2002: 79-86.
[10]	Bing L. Sentiment Analysis and Opinion Mining[M]. Williston: Morgan & Claypool Publishers, 2012.
[11]	Mohammad S M, Kiritchenko S, Zhu X. NRC-Canada: Building the state-of-the-art in sentiment analysis of Tweets[C] //Proceedings of the 7th International Workshop on Semantic Evaluation. Atlanta: Association for Compu- tational Linguistics, 2013: 321-327.
[12]	Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
[13]	Lecun Y, Bottou L, Bengio J, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[14]	Wang X, Liu Y, Sun C, et al. Predicting polarities of Tweets by composing word embeddings with Long Short-Term Memory[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Beijing: Association for Computational Linguistics, 2015: 1343-1353.
[15]	Stojanovski D, Strezoski G, Madjarov G, et al. Twitter Sentiment Analysis Using Deep Convolutional Neural Network [M]. Berlin Heidelberg: Springer-Verlag, 2015.
[16]	Severyn A, Moschitti A. UNITN : Training deep convolu- tional neural network for twitter sentiment classification[J]. Semeval, 2014, 54(2): 161-176.
[17]	Santos d C N, Gatti M. Deep convolutional neural networks for sentiment analysis of short texts[C]// Proceedings of COLING the 25th International Conference on Computational Linguistics: Technical Papers. Dublin: Association for Computational Linguistics, 2014: 69-78.
[18]	Chang J C, Lin C. Recurrent-Neural-Network for Language Detection on Twitter Code-Switching Corpus[EB/OL]. [2018-03-14]. https://arxiv.org/pdf/1412.4314.pdf.
[19]	Joshi A K. Processing of sentences with intra-sentential code-switching[C] // Proceedings of the 9th Conference on Computational Linguistics. Prague: Association for Comput- ational Linguistics, 1982: 145-150.
[20]	Samih Y, Maharjan S, Attia M, et al. Multilingual code-switching identification via LSTM recurrent neural networks [C]// Proceedings of the Second Workshop on Computational Approaches to Code Switching. Austin: Association for Computational Linguistics, 2016: 50-59.
[21]	Collobert R, Weston J, Karlen M, et al. Natural language processing (Almost) from scratch [J]. J Mach Learn Res, 2011, 12: 2493-2537.
[22]	Kim Y, Jernite Y, David S, et al. Character-aware neural language models [C] // Thirtieth AAAI Conf Artif Intell. Phoenix: AAAI Press, 2016: 2741-2749.
[23]	Hinton G E, Srivastava N, Krizhevsky, et al. Improving Neural Networks by Preventing Co-adaptation of Feature Detectors [EB/OL]. [2018-03-14].https://arxiv.org/pdf/1207. 0580. pdf.
[24]	James G, Witten D, Hastie T, et al. An Introduction to Statistical Learning: With Applications in R [M]. Berlin Heidelberg: Springer-Verlag, 2013.
[25]	Brownlee J. What is the Difference Between Test and Validation Datasets?[EB/OL]. [2018-04-06]. https://machine learningmastery.com/difference-test-validation-datasets/.
[26]	MacLachlan Geoffrey J, Do K-A, Ambroise K. Analyzing Microarray Gene Expression Data[M]. Hoboken:Willey, 2013.
[27]	Al-Rfou R, Alain G, Almahairi A, et al. Theano: A Python Framework for Fast Computation of Mathematical Express- ions [EB/OL].[2018-03-14]. https://arxiv.org/pdf/1605. 02688.pdf. 
[28]	Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine learning in Python[J]. J Mach Learn Res, 2012, 12(10): 2825-2830.
Welcome To WUJNS

HOME | Aim and Scope | Editoral Board | Current Issue | Back Issue | Subscribe | Crosscheck | Polishing | Contact us Copyright © 1997-2018 All right reserved