Classification of Sentiment Analysis and Community Opinion Modeling Topics for Application of ICT in Government Operations

Authors

  • Andi Akram Nur Risal Department of Informatics and Computer Engineering, Universitas Negeri Makassar, Makassar 90224, Indonesia
  • Fathahillah Fathahillah Department of Informatics and Computer Engineering, Universitas Negeri Makassar, Makassar 90224, Indonesia
  • Dwi Rezky Anandari Sulaiman Department of Informatics and Computer Engineering, Universitas Negeri Makassar, Makassar 90224, Indonesia

DOI:

https://doi.org/10.55151/ijeedu.v5i1.99

Keywords:

Naïve Bayes, Opinion, Prediction, Sentiment Analysis, Topic Modeling

Abstract

Utilizing information systems is very useful in the current era. Digitizing administration in the Village is beneficial in the service process to the public. This is seen as a change in service that can make it easier or more difficult for the people of Sanrobone Village to take care of administration at the village office. This study aims to analyze public opinion regarding the use of e-government, predict public opinion regarding the use of e-government, and analyze modeling topics related to the use of e-government. This research applies a text mining algorithm with a sentiment analysis method to see positive, negative, and neutral public perceptions and also uses topic modeling to get the most frequently appearing topics in the data. Stages in this study include Data Collection, Text Pre-processing, Sentiment Analysis, Topic Modelling, Classification, and Evaluation. The results obtained are the ten words that appear most often in the responses of the Village community: easy 122, help 96, village 80, accessed 80, letter 80, permit 77, resident 73, manage 60, service 52, and the person with 52 words. The sentiment analysis is positive, with 411 opinions, 37 negative opinions, and 152 neutral opinions. Finally, the performance of the Nave Bayes algorithm in predicting classification results is excellent, with an accuracy rate of 98 percent.

References

S. Sulaiman, A. Nizam, F. Farid, T. M. Kesuma, I. Madjid, and R. Siregar, “The role of good government governance principles in enhancing the performance of public organization in Aceh, Indonesia,” in 1st Aceh Global Conference (AGC 2018), 2019, pp. 731–738.

H. Addink, Good governance: Concept and context. Oxford University Press, 2019.

P. Mehta and S. Pandya, “A review on sentiment analysis methodologies, practices and applications,” Int. J. Sci. Technol. Res., vol. 9, no. 2, pp. 601–609, 2020.

N. C. Dang, M. N. Moreno-García, and F. De la Prieta, “Sentiment analysis based on deep learning: A comparative study,” Electronics, vol. 9, no. 3, p. 483, 2020.

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.

M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artif. Intell. Rev., vol. 55, no. 7, pp. 5731–5780, 2022.

T. Shaik, X. Tao, C. Dann, H. Xie, Y. Li, and L. Galligan, “Sentiment analysis and opinion mining on educational data: A survey,” Nat. Lang. Process. J., vol. 2, p. 100003, 2023, doi: https://doi.org/10.1016/j.nlp.2022.100003.

J. Chen, N. Song, Y. Su, S. Zhao, and Y. Zhang, “Learning user sentiment orientation in social networks for sentiment analysis,” Inf. Sci. (Ny)., vol. 616, pp. 526–538, 2022.

Z. Feng, H. Zhou, Z. Zhu, and K. Mao, “Tailored text augmentation for sentiment analysis,” Expert Syst. Appl., vol. 205, p. 117605, 2022, doi: https://doi.org/10.1016/j.eswa.2022.117605.

Q. A. Xu, V. Chang, and C. Jayne, “A systematic review of social media-based sentiment analysis: Emerging trends and challenges,” Decis. Anal. J., p. 100073, 2022.

D. U. Vidanagama, A. T. P. Silva, and A. S. Karunananda, “Ontology based sentiment analysis for fake review detection,” Expert Syst. Appl., vol. 206, p. 117869, 2022, doi: https://doi.org/10.1016/j.eswa.2022.117869.

F. Xu, Z. Pan, and R. Xia, “E-commerce product review sentiment classification based on a naïve Bayes continuous learning framework,” Inf. Process. Manag., vol. 57, no. 5, p. 102221, 2020.

P. Wang, J. Li, and J. Hou, “S2SAN: A sentence-to-sentence attention network for sentiment analysis of online reviews,” Decis. Support Syst., vol. 149, p. 113603, 2021, doi: https://doi.org/10.1016/j.dss.2021.113603.

P. Mukherjee, Y. Badr, S. Doppalapudi, S. M. Srinivasan, R. S. Sangwan, and R. Sharma, “Effect of Negation in Sentences on Sentiment Analysis and Polarity Detection,” Procedia Comput. Sci., vol. 185, pp. 370–379, 2021, doi: https://doi.org/10.1016/j.procs.2021.05.038.

N. Pavitha et al., “Movie recommendation and sentiment analysis using machine learning,” Glob. Transitions Proc., vol. 3, no. 1, pp. 279–284, 2022, doi: https://doi.org/10.1016/j.gltp.2022.03.012.

Y. Zhang and L. Zhang, “Movie Recommendation Algorithm Based on Sentiment Analysis and LDA,” Procedia Comput. Sci., vol. 199, pp. 871–878, 2022, doi: https://doi.org/10.1016/j.procs.2022.01.109.

C. I. Ossai and N. Wickramasinghe, “Sentiments prediction and thematic analysis for diabetes mobile apps using Embedded Deep Neural Networks and Latent Dirichlet Allocation,” Artif. Intell. Med., vol. 138, p. 102509, 2023, doi: https://doi.org/10.1016/j.artmed.2023.102509.

A. Yadav and D. K. Vishwakarma, “Sentiment analysis using deep learning architectures: a review,” Artif. Intell. Rev., vol. 53, no. 6, pp. 4335–4385, 2020.

M. Birjali, M. Kasri, and A. Beni-Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowledge-Based Syst., vol. 226, p. 107134, 2021.

F. Alshuwaier, A. Areshey, and J. Poon, “Applications and Enhancement of Document-Based Sentiment Analysis in Deep learning Methods: Systematic Literature Review,” Intell. Syst. with Appl., p. 200090, 2022.

M. Araújo, A. Pereira, and F. Benevenuto, “A comparative study of machine translation for multilingual sentence-level sentiment analysis,” Inf. Sci. (Ny)., vol. 512, pp. 1078–1102, 2020.

L. Xu and W. Wang, “Improving aspect-based sentiment analysis with contrastive learning,” Nat. Lang. Process. J., p. 100009, 2023.

M. C. Rahmadan, A. N. Hidayanto, D. S. Ekasari, and B. Purwandari, “Sentiment analysis and topic modelling using the lda method related to the flood disaster in jakarta on twitter,” in 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2020, pp. 126–130.

D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, no. Jan, pp. 993–1022, 2003.

H. Jelodar et al., “Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey,” Multimed. Tools Appl., vol. 78, pp. 15169–15211, 2019.

A. Dag, A. Z. Dag, A. Asilkalkan, S. Simsek, and D. Delen, “A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends,” J. Bus. Res., vol. 156, p. 113522, 2023, doi: https://doi.org/10.1016/j.jbusres.2022.113522.

S. Chen, G. I. Webb, L. Liu, and X. Ma, “A novel selective naïve Bayes algorithm,” Knowledge-Based Syst., vol. 192, p. 105361, 2020.

E. Sutoyo and A. Almaarif, “Educational data mining for predicting student graduation using the naïve bayes classifier algorithm,” J. RESTI (Rekayasa Sist. Dan Teknol. Informasi), vol. 4, no. 1, pp. 95–101, 2020.

J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,” Inf. Sci. (Ny)., vol. 507, pp. 772–794, 2020, doi: https://doi.org/10.1016/j.ins.2019.06.064.

K. Fellnhofer, “Positivity and higher alertness levels facilitate discovery: Longitudinal sentiment analysis of emotions on Twitter,” Technovation, vol. 122, p. 102666, 2023.

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Published

2023-04-29

How to Cite

[1]
A. A. N. . Risal, F. Fathahillah, and D. R. A. . Sulaiman, “Classification of Sentiment Analysis and Community Opinion Modeling Topics for Application of ICT in Government Operations”, Int. J. Environ. Eng. Educ., vol. 5, no. 1, pp. 35–44, Apr. 2023.

Issue

Vol. 5 No. 1 (2023)

Section

Research Article

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