Sentiment Analysis of Student Satisfaction on Telkom University Language Center (LaC) Services on Instagram Using the RNN Method
- Muhammad Juldan Naufal Telkom University, Indonesia
-
Donny Richasdy
*
Telkom University, Bandung,
Indonesia
- Muhammad Arif Bijaksana Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
Social media has become a medium for communication between individuals and aspects of the business, including decision-making processes, brand promotion, brand marketing, and personal branding. One of them is Instagram. Using the comments feature on Instagram, users can communicate and give opinions on an upload on an Instagram account. Sentiment analysis can be done to analyze comments on the LaC (language center) Instagram account to measure student satisfaction sentiment towards Telkom university's LaC (language center) services. This study aims to analyze the sentiment or opinion of student satisfaction with the Telkom University Language Center (LaC) service on Instagram. The author also performs a classification based on positive sentiment, negative, and neutral categories using the Recurrent Neural Network (RNN) method and the Confusion Matrix measurement. From the test results on the model built to get an accuracy value of 79%.
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References
C. Yang, “Research in the Instagram Context: Approaches and Methods,” The Journal of Social Sciences Research, no. 71, pp. 15–21, Feb. 2021, doi: 10.32861/jssr.71.15.21.
McLachlan Stacey, “35 Instagram Stats That Matter to Marketers in 2022,” Hootsuite, Jan. 18, 2022. https://blog.hootsuite.com/instagram-statistics/ (accessed Jul. 28, 2022).
S. Shayaa et al., “Sentiment analysis of big data: Methods, applications, and open challenges,” IEEE Access, vol. 6, pp. 37807–37827, Jun. 2018, doi: 10.1109/ACCESS.2018.2851311.
T. Katte-Bangayya and M. Vinicius, “Recurrent Neural Network and its Various Architecture Types Cite this paper Related papers A Survey on Parallel Processing in a CPU-GPU Collaborat ive Environment Using Ant Colony Opt … Research and Scient ific Innovat ion Societ y RSIS Int ernat ional A Crit ical Review of Recurrent Neural Net works for Sequence Learning Recurrent Neural Network and its Various Architecture Types Trupti Katte,” 2018. [Online]. Available: www.rsisinternational.org
F. Long, K. Zhou, and W. Ou, “Sentiment Analysis of Text Based on Bidirectional LSTM With Multi-Head Attention,” IEEE Access, vol. 7, pp. 141960–141969, 2019, doi: 10.1109/ACCESS.2019.2942614.
Universitas Amikom Yogyakarta, Universitas Gadjah Mada. Departemen Teknik Elektro dan Teknologi Informasi, Sekolah Tinggi Manajemen Informatika dan Komputer AMIKOM Purwokerto, Institute of Electrical and Electronics Engineers. Indonesia Section, and Institute of Electrical and Electronics Engineers, “ICITISEE 2018 : the 3rd 2018 International conferences on Information Technology, Information Systems and Electrical Engineering : proceeding : November 13-14th 2018, Yogyakarta, Indonesia”.
W. Widayat, “Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 3, p. 1018, Jul. 2021, doi: 10.30865/mib.v5i3.3111.
D. Tri Hermanto, A. Setyanto, and E. T. Luthfi, “Algoritma LSTM-CNN untuk Sentimen Klasifikasi dengan Word2vec pada Media Online LSTM-CNN Algorithm for Sentiment Clasification with Word2vec On Online Media”.
A. Patel and A. K. Tiwari, “Sentiment Analysis by using Recurrent Neural Network.” [Online]. Available: https://ssrn.com/abstract=3349572
A. I. Saad, “Opinion Mining on US Airline Twitter Data Using Machine Learning Techniques,” in 2020 16th International Computer Engineering Conference (ICENCO), Dec. 2020, pp. 59–63. doi: 10.1109/ICENCO49778.2020.9357390.
S. Pal, S. Ghosh, and A. Nag, “Sentiment Analysis in the Light of LSTM Recurrent Neural Networks,” International Journal of Synthetic Emotions, vol. 9, no. 1, pp. 33–39, Jan. 2018, doi: 10.4018/IJSE.2018010103.
N. Khan, M. U. Akram, A. Shah, and S. A. Khan, “Important attributes of customer satisfaction in telecom industry: A survey based study,” in 2017 4th IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS), Nov. 2017, pp. 1–7. doi: 10.1109/ICETAS.2017.8277858.
M. M. Rind, A. A. Shaikh, K. Kumar, S. Solangi, and M. A. Chhajro, “Understanding the factors of customer satisfaction: An empirical analysis of Telecom broadband services,” in 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Nov. 2018, pp. 1–4. doi: 10.1109/ICETAS.2018.8629261.
F. Hemmatian and M. K. Sohrabi, “A survey on classification techniques for opinion mining and sentiment analysis,” Artificial Intelligence Review, vol. 52, no. 3, pp. 1495–1545, Oct. 2019, doi: 10.1007/s10462-017-9599-6.
R. C. Staudemeyer, “Applying long short-term memory recurrent neural networks to intrusion detection,” South African Computer Journal, vol. 56, Jul. 2015, doi: 10.18489/sacj.v56i1.248.
P. F. Muhammad, R. Kusumaningrum, and A. Wibowo, “Sentiment Analysis Using Word2vec And Long Short-Term Memory (LSTM) For Indonesian Hotel Reviews,” Procedia Computer Science, vol. 179, pp. 728–735, Jan. 2021, doi: 10.1016/J.PROCS.2021.01.061.
D. Wang, J. Fan, H. Fu, and B. Zhang, “Optimization of Big Data Construction Engineering Quality Management Based on RNN-LSTM,” 2018, doi: 10.1155/2018/9691868.
M. Saiful Anwar, “Prosiding KONFERENSI ILMIAH MAHASISWA UNISSULA (KIMU) 2 SISTEM PENCARIAN E-JOURNAL MENGGUNAKAN METODE STOPWORD REMOVAL DAN STEMMING BERBASIS ANDROID,” 2019.
T. Parlar, S. A. Özel, and F. Song, “QER: a new feature selection method for sentiment analysis,” Human-centric Computing and Information Sciences, vol. 8, no. 1, Dec. 2018, doi: 10.1186/s13673-018-0135-8.
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