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Sentiment Analysis Using Backpropagation Method to Recognize the Public Opinion
https://doi.org/10.22146/ijccs.78664 I Komang Arya Ganda Wiguna(1*), Putu Sugiartawan(2), I Gede Iwan Sudipa(3), I Putu Yudi Pratama(4)
(1) Program Studi Teknik Informatika, Institut Bisnis dan Teknologi Indonesia, Bali
(2) Program Studi Teknik Informatika, Institut Bisnis dan Teknologi Indonesia, Bali
(3) Program Studi Teknik Informatika, Institut Bisnis dan Teknologi Indonesia, Bali
(4) Program Studi Teknik Informatika, Institut Bisnis dan Teknologi Indonesia, Bali
(*) Corresponding Author
Abstract
Improve the service quality of tourism actors by conducting sentiment analysis on digital platforms owned by tourism businesses and collecting negative sentiments to improve the quality of services from companies owned by tourism businesses. The growth of the hospitality industry in Indonesia is experiencing rapid growth every year. The tourism industry, part of the hospitality industry, also does not escape the influence of positive and negative sentiments. One method to perform accurate sentiment analysis is Backpropagation Neural Network. Based on the results of tests on the neural network, the best accuracy is obtained when using one hidden layer with the first layer of 10 neurons. The learning rate is 0.000002, where the accuracy is 71.630%. More epochs do not guarantee better accuracy. Based on the results of the research that has been done, suggestions for further researchers are to analyze the review dataset processing method so that it gets a cleaner dataset and is expected to improve better accuracy.
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[1] J. Ye et al., “Jou,” Knowledge-Based Syst., p. 110021, 2022.
[2] I. Almalis, E. Kouloumpris, and I. Vlahavas, “Sector-level sentiment analysis with deep learning,” Knowledge-Based Syst., vol. 258, p. 109954, 2022.
[3] P. Tao, J. Cheng, and L. Chen, “Brain-inspired chaotic backpropagation for MLP,” Neural Networks, vol. 155, pp. 1–13, 2022.
[4] G. Shen, D. Zhao, and Y. Zeng, “Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks,” Patterns, vol. 3, no. 6, p. 100522, 2022.
[5] A. Glushchenko, V. Petrov, and K. Lastochkin, “Backpropagation method modification using Taylor series to improve accuracy of offline neural network training,” Procedia Comput. Sci., vol. 186, pp. 202–209, 2021.
[6] P. Shang, L. Yang, Y. Yao, L. (Carol) Tong, S. Yang, and X. Mi, “Integrated optimization model for hierarchical service network design and passenger assignment in an urban rail transit network: A Lagrangian duality reformulation and an iterative layered optimization framework based on forward-passing and backpropagatio,” Transp. Res. Part C Emerg. Technol., vol. 144, no. October 2021, p. 103877, 2022.
[7] A. Gandhi, K. Adhvaryu, S. Poria, E. Cambria, and A. Hussain, “Multimodal Sentiment Analysis: A Systematic review of History, Datasets, Multimodal Fusion Methods, Applications, Challenges and Future Directions,” Inf. Fusion, 2022.
[8] N. Leelawat et al., “Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning,” Heliyon, vol. 8, no. 10, p. e10894, 2022.
[9] Y. Bian, R. Ye, J. Zhang, and X. Yan, “Customer preference identification from hotel online reviews: A neural network based fine-grained sentiment analysis,” Comput. Ind. Eng., vol. 172, no. PA, p. 108648, 2022.
[10] R. Chiong, G. S. Budhi, S. Dhakal, and F. Chiong, “A textual-based featuring approach for depression detection using machine learning classifiers and social media texts,” Comput. Biol. Med., vol. 135, no. February, p. 104499, 2021.
[11] S. Demir and B. Topcu, “Graph-based Turkish text normalization and its impact on noisy text processing,” Eng. Sci. Technol. an Int. J., no. xxxx, p. 101192, 2022.
[12] N. R. Bhowmik, M. Arifuzzaman, and M. R. H. Mondal, “Sentiment analysis on Bangla text using extended lexicon dictionary and deep learning algorithms,” Array, vol. 13, p. 100123, 2022.
[13] S. Tehseen, F. U. Ahmed, Z. H. Qureshi, M. J. Uddin, and T. Ramayah, “Entrepreneurial competencies and SMEs’ growth: the mediating role of network competence,” Asia-Pacific J. Bus. Adm., vol. 11, no. 1, pp. 2–29, 2019.
[14] A. M. Saeed, S. R. Hussein, C. M. Ali, and T. A. Rashid, “Medical dataset classification for Kurdish short text over social media,” Data Br., vol. 42, p. 108089, 2022.
[15] Z. Wu, D. Tang, Y. Jiang, Y. Lu, and Y. Qiao, “Learned modified perturbation backpropagation for fiber nonlinear equalization in high-symbol-rate transmission systems,” Opt. Commun., vol. 521, no. June, p. 128612, 2022.
[16] S. Mouloodi, H. Rahmanpanah, S. Gohari, C. Burvill, and H. M. S. Davies, “Feedforward backpropagation artificial neural networks for predicting mechanical responses in complex nonlinear structures: A study on a long bone,” J. Mech. Behav. Biomed. Mater., vol. 128, no. December 2021, p. 105079, 2022.
[17] C. Lubis, E. Sutedjo, and B. Setiadi, “Prediksi Harga Saham Dengan Menggunakan,” Network, vol. 2005, no. Snati, pp. 17–19, 2005.
[18] I. G. Made, N. Desnanjaya, and P. Sugiartawan, “Controlling and Monitoring of Temperature and Humidity of Oyster Mushrooms in Tropical Climates,” no. x.
DOI: https://doi.org/10.22146/ijccs.78664
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