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Analisis Sentimen Pengungkapan Informasi Manajemen: Text Mining Berbasis Metode VADER

Authors

  • Emelia Aprodaid Marwa Universitas Kristen Satya Wacana
  • Ari Budi Kristanto Universitas Kristen Satya Wacana

DOI:

10.33395/owner.v6i3.895

Keywords:

: annual report; negative sentiment; positive sentiment; sentiment analysis

Abstract

Laporan keuangan menyajikan informasi yang berkaitan dengan kondisi keuangan perusahaan. Namun, ada berbagai jenis data yang disediakan untuk membantu kita dalam menilai dan memahami kondisi bisnis perusahaan. Data terkait perusahaan yang paling banyak tersedia adalah dalam bentuk teks. Data ini dapat mencakup laporan tahunan, situs web resmi, atau bahkan posting media sosial yang mungkin berisi data non-keuangan. Informasi nonkeuangan juga penting untuk membantu interpretasi informasi keuangan. Penelitian ini bertujuan untuk menganalisis sentimen diskusi dan analisis manajemen yang dimuat dalam laporan tahunan perusahaan manufaktur. Sampel penelitian adalah perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia (BEI) selama periode 2016-2020 berturut-turut. Ada 102 perusahaan atau 510 observasi penelitian termasuk dalam penelitian ini. Penelitian ini menggunakan teknik analisis sentimen berbasis pendekatan leksikon dengan menggunakan metode VADER. Sedangkan proses analisis sentimen akan dibantu oleh aplikasi Orange Data Mining. Hasil penelitian menunjukkan bahwa pengungkapan positif lebih besar daripada sentimen negatif. Pola sentimen perusahaan yang diklasifikasikan berdasarkan sektor, ukuran perusahaan (total aset dan total penjualan), profitabilitas (ROI), dan likuiditas (rasio lancar) menunjukkan hasil yang relatif sama. Hasil ini menggambarkan bahwa karakteristik perusahaan tidak membuat perbedaan dalam pemilihan kata sentimen pada perusahaan manufaktur.

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References

Aljifri, K. (2008). Annual report disclosure in a developing country: The case of the UAE. Advances in Accounting, 24(1), 93–100. https://doi.org/10.1016/j.adiac.2008.05.001

Arvidsson, S. (2011). Disclosure of non-financial information in the annual report: A management team perspective. Journal of Intellectual Capital, 12(2), 277–300. https://doi.org/10.1108/14691931111123421

Barako, D. G. (2007). Determinants of voluntary disclosures in Kenyan companies annual reports. Determinants of Voluntary Disclosures in Kenyan Companies Annual Reports, 1(August), 113–128.

Baretta, V., Demartini C, M., Lico, L., & Trucco, S. (2021). A tone analysis of the non-financial disclosure in the automotive industry. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2016.11.022

Barus, R., & Maksum, A. (2011). Analisis pengungkapan informasi corporate social responsibility dan pengaruhnya terhadap return saham. Jurnal Akuntansi Dan Auditing Indonesia, 15(1), 83–102.

Camodeca, R., Almici, A., & Sagliaschi, U. (2018). Sustainability disclosure in integrated reporting: Does it matter to investors? A cheap talk approach. Sustainability (Switzerland), 10(12), 1–34. https://doi.org/10.3390/su10124393

Chakraborty, B., & Bhattacharjee, T. (2020). A review on textual analysis of corporate disclosure according to the evolution of different automated methods. Journal of Financial Reporting and Accounting, 18(4), 757–777. https://doi.org/10.1108/JFRA-02-2020-0047

Chen, C., Liu, C., Chang, Y., & Tsai, H. (2013). Opinion mining for relating subjective expressions and annual earnings in US financial statements. 29(3).

Deveikyte, J., Geman, H., Piccari, C., & Provetti, A. (2020). A Sentiment Analysis Approach to the Prediction of Market Volatility. 1–15.

Gandía, J. L., & Huguet, D. (2021). Textual analysis and sentiment analysis in accounting. 24(2), 168–183. https://doi.org/https://www.doi.org/10.6018/rcsar.386541

Goel, S., & Gangolly, J. (2012). Beyond the numbers: Mining the annual reports for hidden cues indicative of financial statement fraud. Intelligent Systems in Accounting, Finance and Management, 19(2), 75–89. https://doi.org/10.1002/isaf.1326

Peraturan Otoritas Jasa Keuangan Republik Indonesia nomor 29/POJK.04/2016 tentang laporan tahunan emiten atau perusahaan publik, 1 (2016). https://doi.org/https://www.ojk.go.id/id/kanal/pasar-modal/regulasi/peraturan-ojk/Documents/Pages/POJK-Laporan-Tahunan-Emiten-Perusahaan-Publik/POJK-Laporan-Tahunan.pdf

Peraturan Otoritas Jasa Keuangan nomor 31 /POJK.04/2015 tentang keterbukaan atas informasi atau fakta material oleh emiten atau perusahaan publik, 1 (2015).

Hájek, P., & Olej, V. (2013). Evaluating sentiment in annual reports for financial distress prediction using neural networks and support vector machines. Communications in Computer and Information Science, 384, 1–10. https://doi.org/10.1007/978-3-642-41016-1_1

Hajek, P., Olej, V., & Myskova, R. (2014). Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making. Technological and Economic Development of Economy, 20(4), 721–738. https://doi.org/10.3846/20294913.2014.979456

Harymawan, I., Nasih, M., Ratri, M. C., Soeprajitno, R. R. W. N., & Shafie, R. (2019). Sentiment analysis trend on sustainability reporting in Indonesia?: Evidence from construction industry. 9(1), 617–630. https://doi.org/https://doi.org/10.9770/jssi.2020.9.3(25)

Hidaya, E. (2008). Pengaruh kualitas pengungkapan informasi terhadap hubungan antara penerapan corporate governance dengan kinerja perusahaan di bursa efek infonesia. 12.

Hutto, C.J. and Gilbert, E. (2014). VADER?: A parsimonious rule-based model for. Eighth International AAAI Conference on Weblogs and Social Media, 18. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/viewPaper/8109

Iglesias, Y., & Andriana, D. (2017). Pengaruh pengungkapan sukarela pada laporan tahunan terhadap kualitas laba pada perusahaan pertambangan. Jurnal ASET (Akuntansi Riset), 9(1), 187. https://doi.org/10.17509/jaset.v9i1.5262

Kaur, A., & Gupta, V. (2013). A survey on sentiment analysis and opinion mining techniques. In Journal of Emerging Technologies in Web Intelligence (Vol. 5, Issue 4). https://doi.org/10.4304/jetwi.5.4.367-371

Kaur, F., & Bhatia, R. (2016). Sentiment analyzing by dictionary based approach. International Journal of Computer Applications, 152(5), 32–34. https://doi.org/10.5120/ijca2016911814

Kearney, C., & Liu, S. (2014). Textual Sentiment In finance: A survey of methods and models. International Review of Financial Analysis, 33(Cc), 171–185. https://doi.org/10.1016/j.irfa.2014.02.006

Magnusson, C., Arppe, A., Eklund, T., Back, B., Vanharanta, H., & Visa, A. (2005). The language of quarterly reports as an indicator of change in the company’s financial status. Information and Management, 42(4), 561–574. https://doi.org/10.1016/j.im.2004.02.008

Maith, H. A. (2013). Analisis laporan keuangan dalam mengukur kinerja keuangan pada PT. Hanjaya Mandala Sampoerna Tbk. Jurnal EMBA, 1(3), 619–628.

Martikainen, M., Miihkinen, A., & Watson, L. (2019). Board characteristics and disclosure tone I . INTRODUCTION.

Mu?ko, P. (2021). Sentiment analysis of CSR disclosures in annual reports of EU companies. Procedia Computer Science, 192, 3351–3359. https://doi.org/10.1016/j.procs.2021.09.108

Pagliarussi, M. S., Aguiar, M. O., & Galdi, F. C. (2016). Sentiment analysis em relatórios anuais de empresas brasileiras com ações negociadas na BM&FBovespa. BASE - Revista de Administração e Contabilidade Da Unisinos, 13(1). https://doi.org/10.4013/base.2016.131.04

Undang-undang Republik Indonesia nomor 40 tahun 2007 tentang perseroan terbatas, 76 (2007). https://www.ojk.go.id/sustainable-finance/id/peraturan/undang-undang/Pages/Undang-Undang-No.-40-tahun-2007-tentang-Perseroan-Terbatas.aspx

Sandoval, A., Gisbert, A., Haya, P., Guerrero, M., & Montoro, H. (2019). Tone analysis in Spanish financial reporting narratives. Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019), September, 42–50. https://www.aclweb.org/anthology/W19-6406

Sharda, R., Delen, D., & Turban, E. (2018). Business Intelligence, Analytics, and Data Science.

Surata, P. A., & Astika, I. B. P. (2019). Reaksi Pasar Atas Pengungkapan Management Discussion and Analysis. 987–1011(1), 1–31.

Varghese, R., & Jayasree. (2013). A survey on sentiment analysis and opinion mining. 312–317.

Vinodhini, G., & Chandrasekaran, R. (2012). Sentiment analysis and opinion mining?: A survey international journal of advanced research in sentiment analysis and opinion mining?: a survey. International Journal of Advanced Research in Computer Science and Software Engineering, 2(6), 283–292.

Yap, R., & Widyaningdyah, U. A. (2009). Pengungkapan pertanggungjawaban sosial pada laporan tahunan perusahaan go publik di Bursa Efek Indonesia (studi empiris perusahaan high dan low profile). 1, 94–105.

Published

2022-05-18

How to Cite

Marwa, E. A. ., & Kristanto, A. B. (2022). Analisis Sentimen Pengungkapan Informasi Manajemen: Text Mining Berbasis Metode VADER. Owner : Riset Dan Jurnal Akuntansi, 6(3). https://doi.org/10.33395/owner.v6i3.895