DOI : https://doi.org/10.18517/ijaseit.12.4.12716

Aspect Based Opinion Summarization Using Rule-Based Method and Support Vector Machine for Indonesian Reviews

Pindya Khabiibah (1), Sari Widya Sihwi (2), Haryono Setiadi (3)
(1) Department of Informatics, Universitas Sebelas Maret, Surakarta, Indonesia
(2) Department of Informatics, Universitas Sebelas Maret, Surakarta, Indonesia
(3) Department of Informatics, Universitas Sebelas Maret, Surakarta, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Khabiibah, Pindya, et al. “Aspect Based Opinion Summarization Using Rule-Based Method and Support Vector Machine for Indonesian Reviews”. International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 4, July 2022, pp. 1387-94, doi:10.18517/ijaseit.12.4.12716.
Citation Format :
E-commerce's reviews feature can help users find information about the desired skin care products to choose the right one. However, the reviews number of a product grows rapidly due to the popularity of e-commerce and the product itself. A user becomes difficult to read all reviews one by one and extract useful information. To deal with this problem, we summarize aspects using the Rule-Based method and Support Vector Machine. We propose a Rule-Based method that is used to break down a review into several segments based on its aspect. Support Vector Machine is used to classify sentence segments according to their polarity. The data used in this study is Indonesian reviews of skin care products obtained from the Female Daily website. The average accuracy results using 10-fold cross-validation of sentiment classification is 74%. We experimented on 462 reviews where the accuracy is 92% in aspect categorization and 71.2% in sentiment classification. Based on humans, the lowest value is the suitability of a sentence with its sentiment/polarity. The highest value is the suitability of sentences with its aspect and usability of summary to helps users to find specific information so they can decide whether to buy that product or not. It can be concluded that the reader can well receive the summary. Future work can consider the negation word to reduce misclassification in the sentiment classification step.

R. E. López Condori and T. A. Salgueiro Pardo, “Opinion summarization methods: Comparing and extending extractive and abstractive approaches,” Expert Syst. Appl., vol. 78, no. C, pp. 124-134, 2017, doi: 10.1016/j.eswa.2017.02.006.

C. Surber and J. Kottner, “Skin care products: What do they promise, what do they deliver,” J. Tissue Viability, vol. 26, no. 1, pp. 29-36, 2017, doi: 10.1016/j.jtv.2016.03.006.

T. A. Tran, J. Duangsuwan, and W. Wettayaprasit, “Automatic Aspect-Based Sentiment Summarization for Visual, Structured, and Textual Summaries,” ECTI Trans. Comput. Inf. Technol., vol. 15, no. 1, pp. 50-72, 2021, doi: 10.37936/ecti-cit.2021151.237565.

M. E. Moussa, E. H. Mohamed, and M. H. Haggag, “A survey on opinion summarization techniques for social media,” Futur. Comput. Informatics J., vol. 3, no. 1, pp. 82-109, 2018, doi: 10.1016/j.fcij.2017.12.002.

C. F. Tsai, K. Chen, Y. H. Hu, and W. K. Chen, “Improving text summarization of online hotel reviews with review helpfulness and sentiment,” Tour. Manag., vol. 80, no. April, p. 104122, 2020, doi: 10.1016/j.tourman.2020.104122.

S. Xiong, K. Wang, D. Ji, and B. Wang, “A short text sentiment-topic model for product reviews,” Neurocomputing, vol. 297, pp. 94-102, 2018, doi: 10.1016/j.neucom.2018.02.034.

R. Mukherjee, P. Goyal, H. C. Peruri, S. Bhattacharya, U. Vishnu, and N. Ganguly, “Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews,” arXiv, pp. 1825-1828, 2020.

W. M. Wang, Z. Li, Z. G. Tian, J. W. Wang, and M. N. Cheng, “Extracting and summarizing affective features and responses from online product descriptions and reviews: A Kansei text mining approach,” Eng. Appl. Artif. Intell., vol. 73, no. October 2017, pp. 149-162, 2018, doi: 10.1016/j.engappai.2018.05.005.

I. El Alaoui, Y. Gahi, R. Messoussi, Y. Chaabi, A. Todoskoff, and A. Kobi, “A novel adaptable approach for sentiment analysis on big social data,” J. Big Data, vol. 5, no. 1, p. 12, 2018, doi: 10.1186/s40537-018-0120-0.

S. Lamsiyah, A. El Mahdaouy, B. Espinasse, and S. El Alaoui Ouatik, “An unsupervised method for extractive multi-document summarization based on centroid approach and sentence embeddings,” Expert Syst. Appl., vol. 167, no. September 2020, p. 114152, 2021, doi: 10.1016/j.eswa.2020.114152.

S. K. D’Mello and J. Kory, “A review and meta-analysis of multimodal affect detection systems,” ACM Comput. Surv., vol. 47, no. 3, pp. 31-38, 2015, doi: 10.1145/2682899.

M. R. Ramadhan, S. N. Endah, and A. B. J. Mantau, “Implementation of Textrank Algorithm in Product Review Summarization,” ICICoS 2020 - Proceeding 4th Int. Conf. Informatics Comput. Sci., 2020, doi: 10.1109/ICICoS51170.2020.9299005.

K. Yauris and M. L. Khodra, “Aspect-based summarization for game review using double propagation,” Proc. - 2017 Int. Conf. Adv. Informatics Concepts, Theory Appl. ICAICTA 2017, pp. 0-5, 2017, doi: 10.1109/ICAICTA.2017.8090997.

Derisma, D. Yendri, and M. Silvana, “Comparing the classification methods of sentiment analysis on a public figure on indonesian-language social media,” J. Theor. Appl. Inf. Technol., vol. 98, no. 8, pp. 1214-1220, 2020.

J. Cervantes, F. Garcia-Lamont, L. Rodrí­guez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189-215, 2020, doi: 10.1016/j.neucom.2019.10.118.

E. Sutoyo and A. Almaarif, “Twitter sentiment analysis of the relocation of Indonesia’s capital city,” Bull. Electr. Eng. Informatics, vol. 9, no. 4, pp. 1620-1630, 2020, doi: 10.11591/eei.v9i4.2352.

G. A. BUNTORO, R. ARIFIN, G. N. SYAIFUDDIIN, A. SELAMAT, O. KREJCAR, and H. FUJITA, “Implementation of a Machine Learning Algorithm for Sentiment Analysis of Indonesia‘s 2019 Presidential Election,” IIUM Eng. J., vol. 22, no. 1, pp. 78-92, 2021, doi: 10.31436/IIUMEJ.V22I1.1532.

F. Binsar and T. Mauritsius, “Mining of Social Media on Covid-19 Big Data Infodemic in Indonesia,” J. Comput. Sci., vol. 16, no. 11, pp. 1598-1609, 2020, doi: 10.3844/JCSSP.2020.1598.1609.

I. G. M. Darmawiguna, G. A. Pradnyana, and I. B. Jyotisananda, “Indonesian sentiment summarization for lecturer learning evaluation by using textrank algorithm,” J. Phys. Conf. Ser. Pap., vol. 1810, no. 012024, 2021, doi: 10.1088/1742-6596/1810/1/012024.

D. Ekawati and M. L. Khodra, “Aspect-based sentiment analysis for Indonesian restaurant reviews,” in Proceedings - 2017 International Conference on Advanced Informatics: Concepts, Theory and Applications, ICAICTA 2017, 2017, pp. 1-6, doi: 10.1109/ICAICTA.2017.8090963.

N. A. Salsabila, Y. Ardhito, W. Ali, A. Septiandri, and A. Jamal, “Colloquial Indonesian Lexicon,” 2018.

B. Santosa, “Data mining teknik pemanfaatan data untuk keperluan bisnis,” Yogyakarta Graha Ilmu, vol. 978, no. 979, p. 756, 2007.

X. Mao, H. Yang, S. Huang, Y. Liu, and R. Li, “Extractive summarization using supervised and unsupervised learning,” Expert Syst. Appl., vol. 133, pp. 173-181, 2019, doi: https://doi.org/10.1016/j.eswa.2019.05.011.

S. A. Babar and P. D. Patil, “Improving performance of text summarization,” Procedia Comput. Sci., vol. 46, no. Icict 2014, pp. 354-363, 2015, doi: 10.1016/j.procs.2015.02.031.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).