Aspect Based Sentiment Analysis Marketplace Product Reviews Using BERT, LSTM, and CNN

Syaiful Imron; Esther Irawati Setiawan; Joan Santoso; Mauridhi Hery Purnomo

doi:10.29207/resti.v7i3.4751

Syaiful Imron Institut Sains dan Teknologi Terpadu Surabaya
Esther Irawati Setiawan Institut Sains dan Teknologi Terpadu Surabaya
Joan Santoso Institut Sains dan Teknologi Terpadu Surabaya
Mauridhi Hery Purnomo Institut Teknologi Sepuluh Nopember

DOI: https://doi.org/10.29207/resti.v7i3.4751

Keywords: Aspect Based Sentiment Analysis, Bert, CNN, LSTM

Abstract

Bukalapak is one of the largest marketplaces in Indonesia. Reviews on Bukalapak are only in the form of text, images, videos, and stars without any special filters. Reading and analyzing manually makes it difficult for potential buyers. To help with this, we can extract this review by using aspect-based sentiment analysis because an entity cannot be represented by just one sentiment. Several previous research stated that using LSTM-CNN got better results than using LSTM or CNN. In addition, using BERT as word embedding gets better results than using word2vec or glove. For this reason, this study aims to classify aspect-based sentiment analysis from the Bukalapak marketplace with BERT as word embedding and using the LSTM-CNN method, where LSTM is for aspect extraction and CNN for sentiment extraction. Based on testing the LSTM-CNN method, it gets better results than LSTM or CNN. The LSTM-CNN model gets an accuracy of 93.91%. Unbalanced dataset distribution can affect model performance. With the increasing number of datasets used, the accuracy of a model will increase. Classification without using stemming on datasets can increase accuracy by 2.04%.

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References

D. L. Rianti, Y. Umaidah, and A. Voutama, “Tren Marketplace Berdasarkan Klasifikasi Ulasan Pelanggan Menggunakan Perbandingan Kernel Support Vector Machine,” STRING, vol. 6, no. 1, p. 98, Aug. 2021. https://doi: 10.30998/string.v6i1.9993.

I. Ventre and D. Kolbe, “The Impact of Perceived Usefulness of Online Reviews, Trust and Perceived Risk on Online Purchase Intention in Emerging Markets: A Mexican Perspective,” Journal of International Consumer Marketing, vol. 32, no. 4, pp. 287–299, Aug. 2020. https://doi: 10.1080/08961530.2020.1712293.

D. F. Nasiri and I. Budi, “Aspect Category Detection on Indonesian E-commerce Mobile Application Review,” in 2019 International Conference on Data and Software Engineering (ICoDSE), Pontianak, Indonesia, pp. 1–6, Nov. 2019. https://doi: 10.1109/ICoDSE48700.2019.9092619.

M. T. Ari Bangsa, S. Priyanta, and Y. Suyanto, “Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network,” Indonesian J. Comput. Cybern. Syst., vol. 14, no. 2, p. 123, Apr. 2020. https://doi: 10.22146/ijccs.51646.

Md. A. Rahman and E. Kumar Dey, “Aspect Extraction from Bangla Reviews using Convolutional Neural Network,” in 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, pp. 262–267, Jun. 2018. htpps://doi: 10.1109/ICIEV.2018.8641050.

Hendra Sahputra Batubara, Nizwardi Jalinus, Waskito, and Ronal Watrianthos, “Study on 11th Grade SMK Imelda Medan Teachers’ and Students’ Perceptions of Online Learning,” Research in Technical and Vocational Education and Training, vol. 1, no. 2, Jun. 2022

M. R. Yanuar and S. Shiramatsu, “Aspect Extraction for Tourist Spot Review in Indonesian Language using BERT,” in 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, pp. 298–302, Feb. 2020. https://doi: 10.1109/ICAIIC48513.2020.9065263.

M. Jiang, W. Zhang, M. Zhang, J. Wu, and T. Wen, “An LSTM-CNN attention approach for aspect-level sentiment classification,” JCM, vol. 19, no. 4, pp. 859–868, Nov. 2019. https://doi: 10.3233/JCM-190022.

I. Priyadarshini and C. Cotton, “A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis,” J Supercomput, vol. 77, no. 12, pp. 13911–13932, Dec. 2021. https://doi: 10.1007/s11227-021-03838-w.

C. Colón-Ruiz and I. Segura-Bedmar, “Comparing deep learning architectures for sentiment analysis on drug reviews,” Journal of Biomedical Informatics, vol. 110, p. 103539, Oct. 2020. https://doi: 10.1016/j.jbi.2020.103539.

A. M. Alayba, V. Palade, M. England, and R. Iqbal, “A Combined CNN and LSTM Model for Arabic Sentiment Analysis,” in Machine Learning and Knowledge Extraction, vol. 11015, A. Holzinger, P. Kieseberg, A. M. Tjoa, and E. Weippl, Eds. Cham: Springer International Publishing, pp. 179–191, 2018. https://doi: 10.1007/978-3-319-99740-7_12.

Samsir, Kusmanto, Abdul Hakim Dalimunthe, Rahmad Aditiya, and Ronal Watrianthos, “Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 1, pp. 1–6, Jun. 2022.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” arXiv, May 2019. http://arxiv.org/abs/1810.04805

W. Meng, Y. Wei, P. Liu, Z. Zhu, and H. Yin, “Aspect Based Sentiment Analysis With Feature Enhanced Attention CNN-BiLSTM,” IEEE Access, vol. 7, pp. 167240–167249, 2019. https://doi: 10.1109/ACCESS.2019.2952888.

P. R. Amalia and E. Winarko, “Aspect-Based Sentiment Analysis on Indonesian Restaurant Review Using a Combination of Convolutional Neural Network and Contextualized Word Embedding,” Indonesian J. Comput. Cybern. Syst., vol. 15, no. 3, p. 285, Jul. 2021.https://doi: 10.22146/ijccs.67306.

R. Man and K. Lin, “Sentiment Analysis Algorithm Based on BERT and Convolutional Neural Network,” in 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), Dalian, China, pp. 769–772, Apr. 2021. https://doi: 10.1109/IPEC51340.2021.9421110.

M. O. Ibrohim and I. Budi, “Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter,” in Proceedings of the Third Workshop on Abusive Language Online, Florence, Italy, pp. 46–57, 2019. https://doi: 10.18653/v1/W19-3506.

N. A. Shafirra and I. Irhamah, “Klasifikasi Sentimen Ulasan Film Indonesia dengan Konversi Speech-to-Text (STT) Menggunakan Metode Convolutional Neural Network (CNN),” JSSITS, vol. 9, no. 1, pp. D95–D101, Jun. 2020. https://doi: 10.12962/j23373520.v9i1.51825.

B. Wilie et al., “IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding,” p. 15. arXiv, Oct. 08, 2020. https://arxiv.org/abs/2009.05387

M. M. Abdelgwad, “Arabic aspect-based sentiment classification using BERT.” arXiv, Nov. 27, 2021. https://arxiv.org/abs/2107.13290

F. A. Prabowo, M. O. Ibrohim, and I. Budi, “Hierarchical Multi-label Classification to Identify Hate Speech and Abusive Language on Indonesian Twitter,” in 2019 6th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), Semarang, Indonesia, pp. 1–5, Sep. 2019. https://doi: 10.1109/ICITACEE.2019.8904425.

K. Sun, Y. Li, D. Deng, and Y. Li, “Multi-Channel CNN Based Inner-Attention for Compound Sentence Relation Classification,” IEEE Access, vol. 7, pp. 141801–141809, 2019. https://doi: 10.1109/ACCESS.2019.2943545.