* Corresponding Author
Hide
Show all
Attention-Based BiLSTM for Negation Handling in Sentimen Analysis
https://doi.org/10.22146/ijccs.60733 Riszki Wijayatun Pratiwi(1*), Yunita Sari(2), Yohanes Suyanto(3)
(1) Master Program of Computer Science, FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(3) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Research on sentiment analysis in recent years has increased. However, in sentiment analysis research there are still few ideas about the handling of negation, one of which is in the Indonesian sentence. This results in sentences that contain elements of the word negation have not found the exact polarity.
The purpose of this research is to analyze the effect of the negation word in Indonesian. Based on positive, neutral and negative classes, using attention-based Long Short Term Memory and word2vec feature extraction method with continuous bag-of-word (CBOW) architecture. The dataset used is data from Twitter. Model performance is seen in the accuracy value.
The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) methods obtained an accuracy of 78.16% and for BiLSTM resulted in an accuracy of 79.68%. whereas in the FSW algorithm is 73.50% and FWL 73.79%. It can be concluded that attention based BiLSTM has the highest accuracy, but the addition of layer attention in the Long Short Term Memory method is not too significant for negation handling. because the addition of the attention layer cannot determine the words that you want to pay attention to.
Keywords
Full Text:
PDFReferences
[1] C. Sammut and G. I. Webb, Eds., Encyclopedia of Machine Learning and Data Mining. Boston, MA: Springer US, 2017.
[2] N. Saputra, “Analisis Sentimen Berbasis Lexicon dan Emoticon,” Gadjah Mada University, Yogyakarta, 2015.
[3] F. T. Al-Khawaldeh, “Speculation and Negation Detection for Arabic Biomedical Texts,” p. 5, 2019.
[4] A. M. Ningtyas, “Pengaruh Penanganan Negasi dalam Bahas Indonesia untuk Pelabelan otomatis pada Analisis Sentimen Twitter,” Gadjah Mada University, Yogyakarta, 2016.
[5] C. Olah and S. Carter, “Attention and Augmented Recurrent Neural,” 2016. https://distill.pub/2016/augmented-rnns/.
[6] L. Chen, “Attention-Based Deep Learning System for Negation and Assertion Detection in Clinical Notes,” Int. J. Artif. Intell. Appl., vol. 10, no. 01, pp. 1–9, Jan. 2019, doi: 10.5121/ijaia.2019.10101.
[7] M. A. Nurrohmat and A. Sn, “Sentiment Analysis of Novel Review Using Long Short- Term Memory Method,” IJCCS Indones. J. Comput. Cybern. Syst., vol. 13, no. 3, p. 209, Jul. 2019, doi: 10.22146/ijccs.41236.
[8] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” p. 9.
[9] A. R. Isnain, A. Sihabuddin, and Y. Suyanto, “Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection,” IJCCS Indones. J. Comput. Cybern. Syst., vol. 14, no. 2, p. 169, Apr. 2020, doi: 10.22146/ijccs.51743.
[10] C. Olah, “Understading LSTM Networks.” http://colah.github.io/posts/2015- 08- Understanding-LSTMs/.
[11] G. I. Winata, O. P. Kampman, and P. Fung, “Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Apr. 2018, pp. 6204–6208, doi: 10.1109/ICASSP.2018.8461990.
DOI: https://doi.org/10.22146/ijccs.60733
Article Metrics
Abstract views : 2719
| 
views : 2196
Refbacks
- There are currently no refbacks.
Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1