Leveraging BiLSTM and LDA for Analyzing and Dashboarding User Feedback in Applications

 (*)Siti Mutmainah Mail (Universitas Islam Indonesia, Yogyakarta, Indonesia)
 Dhomas Hatta Fudholi (Universitas Islam Indonesia, Yogyakarta, Indonesia)

(*) Corresponding Author

Submitted: November 20, 2023; Published: January 9, 2024


The idea of prioritizing customer satisfaction to uphold or improve the excellence of a product or service can incorporate the utilization of user feedback. However, to provide a comprehensive visual summary to application developers or stakeholders, it is important to provide a detailed description of user sentiment issues. In this study, the data source used is user feedback from local telemedicine applications in Indonesia. This research builds a framework of deep learning to perform user feedback analysis and applies topic modeling to sentiment clusters. then builds visual construction of research results effectively and efficiently, to facilitate stakeholders in making decisions. Build a framework to analyze user feedback utilizing deep learning BiLSTM + IndoBERT for sentiment classification and LDA to model topics in sentiment groups. The results show that most of the user reviews of the five telemedicine applications have a positive sentiment at 91%. The model used has good prediction performance with the accuracy of the BiLSTM model with IndoBERT 96.44%. The negative sentiment group comprises 12 topics (0.58446), the most significant topics being 35.4% about telephone broadcasting, 25.3% payments, and 8.5% about medicine purchase service. For the imbalanced data case, BiLSTM showed good accuracy and precision values. The classifications and topics generated by deep learning models are affected by improper data labeling, so it is necessary to explore the data labels generated.


Sentiment Analysis; Topic Modeling; Telemedicine; BiLSTM; LDA; IndoBERT

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V. Nurcahyawati and Z. Mustaffa, Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation, J. Inf. Syst. Eng. Bus. Intell., vol. 9, no. 1, pp. 108118, 2023, doi: 10.20473/jisebi.9.1.108-118.

M. P. Abraham, Feature Based Sentiment Analysis of Mobile Product Reviews using Machine Learning Techniques, Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 2, pp. 22892296, 2020, doi: 10.30534/ijatcse/2020/210922020.

K. Khan, B. Baharudin, A. Khan, and A. Ullah, Mining opinion components from unstructured reviews: A review, J. King Saud Univ. - Comput. Inf. Sci., vol. 26, no. 3, pp. 258275, 2014, doi: 10.1016/j.jksuci.2014.03.009.

O. Oyebode, F. Alqahtani, and R. Orji, Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews, IEEE Access, vol. 8, pp. 111141111158, 2020, doi: 10.1109/ACCESS.2020.3002176.

M. B. N. Taj and G. S. Girisha, Insights of strength and weakness of evolving methodologies of sentiment analysis, Glob. Transitions Proc., vol. 2, no. 2, pp. 157162, 2021, doi: 10.1016/j.gltp.2021.08.059.

M. Cleary, Deep Learning A Practioners Approach, vol. 53, no. 9. 2017.

S. Ali, G. Wang, and S. Riaz, Aspect based sentiment analysis of ridesharing platform reviews for kansei engineering, IEEE Access, vol. 8, pp. 173186173196, 2020, doi: 10.1109/ACCESS.2020.3025823.

G. Nuli, A. F. Hidayatullah, and R. Rahmadi, Comparison of Machine Learning and Deep Learning Algorithms for Named Entity Recognition: Case Study of Disaster Data, Indones. J. Appl. Informatics, vol. 4, no. 2, p. 138, 2020, doi: 10.20961/ijai.v4i2.41317.

G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, Sentiment analysis of comment texts based on BiLSTM, IEEE Access, vol. 7, pp. 5152251532, 2019, doi: 10.1109/ACCESS.2019.2909919.

S. Siami-Namini, N. Tavakoli, and A. S. Namin, The Performance of LSTM and BiLSTM in Forecasting Time Series, Proc. - 2019 IEEE Int. Conf. Big Data, Big Data 2019, pp. 32853292, 2019, doi: 10.1109/BigData47090.2019.9005997.

Y. Dong, Y. Fu, L. Wang, Y. Chen, Y. Dong, and J. Li, A sentiment analysis method of capsule network based on BiLSTM, IEEE Access, vol. 8, pp. 3701437020, 2020, doi: 10.1109/ACCESS.2020.2973711.

H. T. Phan, V. C. Tran, N. T. Nguyen, and D. Hwang, Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model, IEEE Access, vol. 8, pp. 1463014641, 2020, doi: 10.1109/ACCESS.2019.2963702.

N. Braig, A. Benz, S. Voth, J. Breitenbach, and R. Buettner, Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data, IEEE Access, vol. 11, no. November 2022, pp. 1477814803, 2023, doi: 10.1109/ACCESS.2023.3242234.

M. N. Uddin, B. Li, Z. Ali, P. Kefalas, I. Khan, and I. Zada, Software defect prediction employing BiLSTM and BERT-based semantic feature, Soft Comput., vol. 26, no. 16, pp. 78777891, 2022, doi: 10.1007/s00500-022-06830-5.

A. Rahmawati, A. Alamsyah, and A. Romadhony, Hoax News Detection Analysis using IndoBERT Deep Learning Methodology, 2022 10th Int. Conf. Inf. Commun. Technol. ICoICT 2022, pp. 368373, 2022, doi: 10.1109/ICoICT55009.2022.9914902.

L. Geni, E. Yulianti, and D. I. Sensuse, Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using IndoBERT Language Models, J. Ilm. Tek. Elektro Komput. dan Inform., vol. 9, no. 3, pp. 746757, 2024, doi: 10.26555/jiteki.v9i3.26490.

Janu Akrama Wardhana and Yuliant Sibaroni, Aspect Level Sentiment Analysis on Zoom Cloud Meetings App Review Using LDA, J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 631638, 2021, doi: 10.29207/resti.v5i4.3143.

N. L. P. M. Putu, Ahmad Zuli Amrullah, and Ismarmiaty, Sentiment Analysis and Lombok Tourism Topic Modeling Using Naive Bayes and Latent Dirichlet Allocation Algorithms, J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 123131, 2021, doi: 10.29207/resti.v5i1.2587.

H. A. Prakosa, A. B. Riyanto, and S. Nasiroh, Sentiment analysis and topic modeling of the Covid-19 pandemic on Twitter social media using Nave Bayes Classifier and Latent Dirichlet Allocation, Jnanaloka, pp. 7378, 2021, doi: 10.36802/jnanaloka.2021.v2-no2-73-78.

M. Sakiyama, N. Fujii, D. Kokuryo, and T. Kaihara, Visualization of group discussion using correspondence analysis and LDA in Ideathon, Procedia CIRP, vol. 88, no. July 2019, pp. 595599, 2020, doi: 10.1016/j.procir.2020.05.104.

S. Mutmainah, D. H. Fudholi, and S. Hidayat, Sentiment Analysis and Topic Modeling of Telemedicine Applications on Google Play Using BiLSTM and LDA, vol. 7, pp. 312323, 2023, doi: 10.30865/mib.v7i1.5486.

R. Alviani, B. Purwandari, I. Eitiveni, and M. Purwaningsih, Factors Affecting Adoption of Telemedicine for Virtual Healthcare Services in Indonesia, J. Inf. Syst. Eng. Bus. Intell., vol. 9, no. 1, pp. 4769, 2023, doi: 10.20473/jisebi.9.1.47-69.

A. Campbell, Introduction to Telemedicine Introduction to Telemedicine, vol. 20, no. 46. 2006.

M. Kay, J. Santos, and M. Takane, Telemedicine: Opportunities and developments in Member States, Observatory, vol. 2, p. 96, 2010, [Online]. Available: http://www.who.int/goe/publications/goe_telemedicine_2010.pdf.

A. Ardyles and Y. Ilyas, Analysis of the Influence of the Covid-19 Pandemic as a Catalyst in the Development of Telemedicine in Indonesia: A Narrative Review, J. Kesehat. Masy., vol. 10, no. 1, pp. 3137, 2022, doi: 10.14710/jkm.v10i1.31609.

World Health Organization, Implementing telemedicine services during COVID-19: guiding principles and considerations for a stepwise approach, https://www.who.int/publications/i/item/WPR-DSE-2020-032, pp. 125, 2020, [Online]. Available: https://apps.who.int/iris/handle/10665/336862.

statcounter, Mobile Operating System Market Share Indonesia june 2021-june 2022, 2022. https://gs.statcounter.com/os-market-share/mobile/indonesia (accessed Jul. 06, 2022).

Z. Hameed and B. Garcia-Zapirain, Sentiment Classification Using a Single-Layered BiLSTM Model, IEEE Access, vol. 8, pp. 7399274001, 2020, doi: 10.1109/ACCESS.2020.2988550.

S. Imron, E. I. Setiawan, and J. Santoso, Deteksi Aspek Review E-Commerce Menggunakan IndoBERT Embedding dan CNN, J. Intell. Syst. Comput., vol. 5, no. 1, pp. 1016, 2023, doi: 10.52985/insyst.v5i1.267.

Fransiscus and A. S. Girsang, Sentiment Analysis of COVID-19 Public Activity Restriction (PPKM) Impact using BERT Method, Int. J. Eng. Trends Technol., vol. 70, no. 12, pp. 281288, 2022, doi: 10.14445/22315381/IJETT-V70I12P226.

J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 1, pp. 41714186, 2019.

F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP, COLING 2020 - 28th Int. Conf. Comput. Linguist. Proc. Conf., pp. 757770, 2020, doi: 10.18653/v1/2020.coling-main.66.

Y. Zhang and L. Zhang, Movie Recommendation Algorithm Based on Sentiment Analysis and LDA, Procedia Comput. Sci., vol. 199, pp. 871878, 2022, doi: 10.1016/j.procs.2022.01.109.

B. Gunawan, H. S. Pratiwi, and E. E. Pratama, Sentiment Analysis System for Product Reviews Using the Naive Bayes Method, J. Edukasi dan Penelit. Inform., vol. 4, no. 2, p. 113, 2018, doi: 10.26418/jp.v4i2.27526.

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