Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network

https://doi.org/10.22146/ijccs.51646

MHD Theo Ari Bangsa(1*), Sigit Priyanta(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


Most online stores provide product review facilities that contain responses to a product. The number of reviews makes it difficult for potential customers to make conclusions, so that sentiment analysis is needed to extract information from these reviews. Most sentiment analysis is done at the document level, so the results were still lacking in detail because the classification is based on the entire sentence or document and does not identify the specific aspect discussed. This research aims to classify aspect-based sentiments from online store reviews using the convolutional neural network (CNN) method with the extraction of features using Word2Vec. The dataset used is Indonesian review data from the site bukalapak.com. The test results on the built system showed that CNN's method of Word2Vec feature extraction has a better score than the naive bayes method with an accuracy value of 85.54%, 96.12% precision, 88.39% recall, and f-measure 92.02%. Classification without using stemming preprocessing on the dataset increases the accuracy by 2.77%.

Keywords


aspect-based sentiment analysis; convolutional neural network; online store

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References

[1]      B. Liu, “Sentiment analysis and opinion mining,” Synth. Lect. Hum. Lang. Technol., vol. 5, no. 1, pp. 1–167, 2012.

[2]      Z. Su, H. Xu, D. Zhang, and Y. Xu, “Chinese sentiment classification using a neural network tool - Word2vec,” in 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), 2014, pp. 1–6.

[3]      M. A. Nasichuddin, “Pengaruh Matriks Filter, Kerangka, dan Pra Pelatihan pada Peningkatan Kinerja Pelatihan CNN Untuk Analisis Sentimen,” Universitas Gadjah Mada, 2018.

[4]      X. Ouyang, P. Zhou, C. H. Li, and L. Liu, “Sentiment Analysis Using Convolutional Neural Network,” in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015, pp. 2359–2364.

[5]      R. Socher, Y. Bengio, and C. Manning, Deep Learning for Natural Language Processing (without Magic). 2012.

[6]      F. Ratnawati and E. Winarko, “Sentiment Analysis of Movie Opinion in Twitter Using Dynamic Convolutional Neural Network Algorithm,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 12, no. 1, p. 1, 2018.

[7]      L. Akhtyamova, J. Cardiff, and M. Alexandrov, “Adverse drug extraction in twitter data using convolutional neural network,” Proc. - Int. Work. Database Expert Syst. Appl. DEXA, vol. 2017-Augus, pp. 88–92, 2017.

[8]      Z. Fachrina and D. H. Widyantoro, “Aspect-sentiment classification in opinion mining using the combination of rule-based and machine learning,” Proc. 2017 Int. Conf. Data Softw. Eng. ICoDSE 2017, pp. 1–6, 2017.

[9]      S. Gojali and M. L. Khodra, “Aspect based sentiment analysis for review rating prediction,” 4th IGNITE Conf. 2016 Int. Conf. Adv. Informatics Concepts, Theory Appl. ICAICTA 2016, 2016.

[10]    Y. T. Pratama, F. A. Bachtiar, and N. Y. Setiawan, “Analisis Sentimen Opini Pelanggan Terhadap Aspek Pariwisata Pantai Malang Selatan Menggunakan TF-IDF dan Support Vector Machine,” vol. 2, no. 12, pp. 6244–6252, 2018.

[11]    T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv Prepr. arXiv1301.3781, 2013.

[12]    Y. Zhang and B. Wallace, “A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification,” arXiv Prepr. arXiv1510.03820, 2015.



DOI: https://doi.org/10.22146/ijccs.51646

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