Two-Stage Sentiment Analysis on Indonesian Online News Using Lexicon-Based
A study case at online new unilateral layoffs at Company XYZ
DOI:
10.33395/sinkron.v8i4.12769Keywords:
InSet, Lexicon Indonesia Dictionary, Sentiment Analysis, Sentiment Indonesia News, SentiStrength_ID, Two Stages Sentiment Analysis, VADER Lexicon basedAbstract
The image of a supplier company is often associated with the well-known brand it supplies, and its reputation can be influenced by online news circulation. To maintain a positive image, it is crucial for the company to monitor and manage online news to rectify any false information. Failure to maintain a good company image can lead to customer order loss and even company shutdown.
This paper aims to conduct a two-stage sentiment analysis on Indonesian news articles regarding unilateral layoffs by company XYZ. The first stage will analyze sentiment in the circulating news about the layoffs, while the second stage will assess sentiment after the company releases a press release to provide accurate information. The VADER lexicon-based method, utilizing the InSet and SentiStrength_ID Indonesian dictionaries, will be employed to analyze sentiment before and after the press release. This will enable us to compare sentiment and evaluate the effectiveness of the press release and the Indonesian dictionaries in analyzing sentiment in the news. The research findings indicate that the company's press release, aimed at correcting false information, had a positive impact by reducing negative sentiment and generating a more positive sentiment in the second stage. Moreover, the selection of the sentiment analysis dictionary also plays a critical role in determining the sentiment analysis results.
Downloads
References
Abdillah, W. F., Premana, A., & Bhakti, R. M. H. (2021). Analisis Sentimen Penanganan Covid-19 dengan Support Vector Machine: Evaluasi Leksikon dan Metode Ekstraksi Fitur. Jurnal Ilmiah Intech : Information Technology Journal of UMUS, 3(02). Retrieved from https://doi.org/10.46772/intech.v3i02.556
Alonso, M. A., Vilares, D., Gómez-Rodríguez, C., & Vilares, J. (2021). Sentiment analysis for fake news detection. Electronics (Switzerland). Retrieved from https://doi.org/10.3390/electronics10111348
Berka, P. (2020). Sentiment analysis using rule-based and case-based reasoning. Journal of Intelligent Information Systems, 55(1). Retrieved from https://doi.org/10.1007/s10844-019-00591-8
Ding, X., Liu, B., & Yu, P. S. (2008). A holistic lexicon-based approach to opinion mining. In WSDM’08 - Proceedings of the 2008 International Conference on Web Search and Data Mining. Retrieved from https://doi.org/10.1145/1341531.1341561
Fauziah, Y., Yuwono, B., & Aribowo, A. S. (2021). Lexicon Based Sentiment Analysis in Indonesia Languages : A Systematic Literature Review. RSF Conference Series: Engineering and Technology, 1(1). Retrieved from https://doi.org/10.31098/cset.v1i1.397
Ferry Sandi, C. I. (2023). Pengakuan Bos Pabrik Adidas di Tangerang PHK 1.400 Karyawan.
Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014. Retrieved from https://doi.org/10.1609/icwsm.v8i1.14550
Koto, F., & Rahmaningtyas, G. Y. (2018). Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs. In Proceedings of the 2017 International Conference on Asian Language Processing, IALP 2017 (Vol. 2018-January). Retrieved from https://doi.org/10.1109/IALP.2017.8300625
Mishra, A., & Samu, S. (2021). Impact of fake news on social image perceptions and consumers’ behavioral intentions. Journal of Consumer Marketing, 38(6). Retrieved from https://doi.org/10.1108/JCM-05-2020-3857
Neal Dickert, Jr., Hatzivassiloglou, V., McKeown, K. R., Ilmania, A., Abdurrahman, Cahyawijaya, S., … Alam, K. M. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14).”. ArXiv, 6(December).
Ray, P., & Chakrabarti, A. (2022). A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis. Applied Computing and Informatics, 18(1–2). Retrieved from https://doi.org/10.1016/j.aci.2019.02.002
Samizade, R., & Abad, E. M. S. (2018). The application of machine learning algorithms for text mining based on sentiment analysis approach. Journal of Information Technology Management. Retrieved from https://doi.org/10.22059/jitm.2017.215513.1807
Seo, S., Kim, C., Kim, H., Mo, K., & Kang, P. (2020). Comparative Study of Deep Learning-Based Sentiment Classification. IEEE Access, 8. Retrieved from https://doi.org/10.1109/ACCESS.2019.2963426
Serrano-Guerrero, J., Olivas, J. A., Romero, F. P., & Herrera-Viedma, E. (2015). Sentiment analysis: A review and comparative analysis of web services. Information Sciences, 311. Retrieved from https://doi.org/10.1016/j.ins.2015.03.040
Wahid, D. H., & SN, A. (2016). Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 10(2). Retrieved from https://doi.org/10.22146/ijccs.16625
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7). Retrieved from https://doi.org/10.1007/s10462-022-10144-1
Zahoor, S., & Rohilla, R. (2020). Twitter Sentiment Analysis Using Lexical or Rule Based Approach: A Case Study. In ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions). Retrieved from https://doi.org/10.1109/ICRITO48877.2020.9197910
Downloads
|
How to Cite
Issue
Section
License
Copyright (c) 2023 Vinardo, Ito Wasito
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
Google Scholar 
Moraref
PKP Index
Garuda Kemdikbud
Indonesia OneSearch
OCLC Worldcat
Dimensions
Index Copernicus
Scilit
