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http://dx.doi.org/10.15207/JKCS.2020.11.11.033

E-commerce data based Sentiment Analysis Model Implementation using Natural Language Processing Model  

Choi, Jun-Young (Graduates School of Computer & Information Technology, Korea University)
Lim, Heui-Seok (Department of Computer Science and Engineering, Korea University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.11, 2020 , pp. 33-39 More about this Journal
Abstract
In the field of Natural Language Processing, Various research such as Translation, POS Tagging, Q&A, and Sentiment Analysis are globally being carried out. Sentiment Analysis shows high classification performance for English single-domain datasets by pretrained sentence embedding models. In this thesis, the classification performance is compared by Korean E-commerce online dataset with various domain attributes and 6 Neural-Net models are built as BOW (Bag Of Word), LSTM[1], Attention, CNN[2], ELMo[3], and BERT(KoBERT)[4]. It has been confirmed that the performance of pretrained sentence embedding models are higher than word embedding models. In addition, practical Neural-Net model composition is proposed after comparing classification performance on dataset with 17 categories. Furthermore, the way of compressing sentence embedding model is mentioned as future work, considering inference time against model capacity on real-time service.
Keywords
NLP; BERT; KoBERT; ELMo; LSTM; CNN;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 D. Hendrycks & K. Gimpel. (2016). Gaussian Error Linear Units (GELUs). https://arxiv.org/abs/1606.08415
2 M. A. Gordon - All The Ways to Compress http://mitchgordon.me/machine/learning/2019/11/18/all-the-ways-to-compress-BERT.html
3 D. Bahdanau, K. H. Cho & Y. Bengio (2014) Neural Machine Translation by Jointly Learning to Align and Translate. ICLR 2015. https://arxiv.org/abs/1409.0473
4 Y. Kim. (2014) Convolutional Neural Networks for Sentence Classification. EMNLP 2014. https://arxiv.org/abs/1408.5882
5 Vaswani et al. (2017). Attention is all you need. https://arxiv.org/abs/1706.03762
6 T. Kudo & J. Richardson. (2018). SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing. EMNLP2018, 66-71 https://arxiv.org/abs/1808.06226
7 A. Joulin. (2016). FastText.zip: Compressing text classification models. ICLR 2017. https://arxiv.org/abs/1612.03651
8 A. F. Agarap. (2018). Deep Learning using Rectified Linear Units (ReLU), 1, 2-8. https://arxiv.org/abs/1803.08375
9 D. P. Kingma & J. Ba. (2014). Adam: A Method for Stochastic Optimization. 1-15. https://doi.org/http://doi.acm.org.ezproxy.lib.ucf.edu/10.1145/1830483.1830503
10 M. Peters. (2018). ELMo-Deep contextualized word representations. NAACL 2018. https://arxiv.org/abs/1802.05365
11 SKTBrain, KoBERT. (2019). https://github.com/SKTBrain/KoBERT
12 J. Devlin, K. Lee & K. Toutanova. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. http://arxiv.org/abs/1810.04805
13 Hochreiter & Schmidhuber. (1997). LONG SHORT-TERM MEMORY. Neural Computation, DOI: 10.1162/neco.1997.9.8.1735   DOI
14 Lecun. (1998). Gradient-Based Learning Applied to Document Recognition. IEEE, 86(11), 2278-2324. DOI:10.1109/5.726791   DOI
15 H. M. Kim & K. B. Park. (2019). Sentiment analysis of online food product review using ensemble technique. Journal of Digital Convergence, 17(4), 115-122. DOI: 10.14400/JDC.2019.17.4.11   DOI
16 H. Y. Park & K. J. Kim. (2019). Sentiment Analysis of Movie Review Using Integrated CNN-LSTM Model. Journal of Intelligence and Information Systems, 25(4), 141-154. DOI : 10.13088/jiis.2019.25.4.141   DOI