DOI: https://doi.org/10.21456/vol14iss4pp353-362

Impact Of Sarcasm Detection on Sentiment Analysis Using Bi-LSTM and FastText

Junita Amalia orcid scopus  -  Information System, Institut Teknologi Del, Sitoluama, Indonesia
*Dian Filia Matondang orcid  -  Information System, Institut Teknologi Del, Sitoluama, Indonesia
Gibert E.M. Hutajulu  -  Information System, Institut Teknologi Del, Sitoluama, Indonesia
Agustina Hasibuan  -  Information System, Institut Teknologi Del, Sitoluama, Indonesia
Open Access Copyright (c) 2024 Jurnal Sistem Informasi Bisnis

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Abstract

Sentiment analysis categorizes a collection of texts in a document as either positive or negative. However, sometimes it cannot give accurate results due to sarcastic sentences. Sarcasm involves the use of positive language to convey negative meanings, So sarcasm detection is needed for sentiment classification to provide better results. One method that can be used to perform Sentiment classification is Bidirectional Long Short-Term Memory (Bi-LSTM). However, text data cannot be processed by Bi-LSTM, so it requires word embedding to convert text data into vectors. In this study, the word embedding used is FastText because it can learn the form of words by considering subword information. The results showed that sentiment classification with sarcasm detection could improve evaluation results by 0.08 for precision, 0.07 for recall, 0.07 for F1-score, and 0.07 for accuracy. A paired sample t-test was conducted on precision, recall, F1-score, and accuracy to examine whether there is a difference between sentiment classification with and without sarcasm detection. The obtained p-values are 2.84.10-9, 4.63.10-7, and 2.40.10-8, 6.22.10-8, respectively. This indicates a difference between sentiment classification with and without sarcasm detection. Therefore, with a 95% confidence level, it can be concluded that sarcasm detection impacts sentiment classification.

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Keywords: Bidirectional Long Short-Term Memory; FastText; Accuracy; Sentiment; Sarcasm; Classification.
Funding: Institut Teknologi Del

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Section: Research Articles
Language : EN
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  1. Alfariqi, F., Maharani, W., & Husen, J. H. (2020). Klasifikasi Sentimen pada Twitter dalam Membantu Pemilihan Kandidat Karyawan dengan Menggunakan Convolutional Neural Network dan Fasttext Embeddings. E-Proceeding of Engineering, 7(2), 8052–8062
  2. Alita, D., & Isnain, A. R. (2020). Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier. Jurnal Komputasi, 8(2), 50–58. https://doi.org/10.23960/komputasi.v8i2.2615
  3. Amalia, J., Fitriyaningsih, I., & Agnesia, Y. (2023). Buku Ajar Probabilitas dan Statistika
  4. Aritonang, Y. V., Napitupulu, D. P., Sinaga, M. H., & Amalia, J. (2022). Pengaruh Hyperparameter pada Fasttext terhadap Performa Model Deteksi Sarkasme Berbasis Bi-LSTM. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(3), 2612–2625. https://doi.org/10.35957/jatisi.v9i3.1331
  5. Augustyniak, L., Kajdanowicz, T., & Kazienko, P. (2019). Aspect detection using word and char embeddings with (Bi) LSTM and CRF. Proceedings - IEEE 2nd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2019, 43–50. https://doi.org/10.1109/AIKE.2019.00016
  6. Dharma, E. M., Gaol, F. L., Warnars, H. L. H. S., & Soewito, B. (2022). the Accuracy Comparison Among Word2Vec, Glove, and Fasttext Towards Convolution Neural Network (Cnn) Text Classification. Journal of Theoretical and Applied Information Technology, 100(2), 349–359
  7. Djam’an, N., Asdar, Nasullah, Djadir, & Fauzan, M. (2021). The Effect of Online Learning Using Zoom on Students’ Learning Outcomes. International Conference on Educational Studies in Mathematics (ICoESM 2021), 611(ICoESM), 92–96
  8. Düntsch, I., & Gediga, G. (2019). Confusion Matrices and Rough Set Data Analysis. Journal of Physics: Conference Series, 1229(1). https://doi.org/10.1088/1742-6596/1229/1/012055
  9. Garcia, S., Luengo, J., & Herrera, F. (2015). Data Preprocessing in Data Mining. Intelligent Systems Reference Library. 2015. In Doi (Vol. 10). http://www.springer.com/series/8578
  10. Han, J., Kamber, M., & Pei, J. (2012). Third Edition : Data Mining Concepts and Techniques. Journal of Chemical Information and Modeling, 53(9), 1689–1699. http://library.books24x7.com/toc.aspx?bkid=44712
  11. Hasnain, M., Pasha, M. F., Ghani, I., Imran, M., Alzahrani, M. Y., & Budiarto, R. (2020). Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking. IEEE Access, 8, 90847–90861. https://doi.org/10.1109/ACCESS.2020.2994222
  12. Kumar, A., Narapareddy, V. T., Srikanth, V. A., Malapati, A., & Neti, L. B. M. (2020). Sarcasm Detection Using Multi-Head Attention Based Bidirectional LSTM. IEEE Access, 8, 6388–6397. https://doi.org/10.1109/ACCESS.2019.2963630
  13. Kurbatov, Y., Rytsarev, I., & Kupriyanov, A. (2020). Research of Text Data Processing Algorithms in Social Networks. Proceedings of ITNT 2020 - 6th IEEE International Conference on Information Technology and Nanotechnology, 1, 2020–2022. https://doi.org/10.1109/ITNT49337.2020.9253271
  14. Lim, E., Setiawan, E. I., & Santoso, J. (2019). Stance Classification Post Kesehatan di Media Sosial Dengan FastText Embedding dan Deep Learning. Journal of Intelligent System and Computation, 1(2), 65–73. https://doi.org/10.52985/insyst.v1i2.86
  15. Malmia, W., Makatita, S. H., Lisaholit, S., Azwan, A., Magfirah, I., Tinggapi, H., & Umanailo, M. C. B. (2019). Problem-based learning as an effort to improve student learning outcomes. International Journal of Scientific and Technology Research, 8(9), 1140–1143
  16. Mishra, P., Singh, U., Pandey, C. M., Mishra, P., & Pandey, G. (2019). Application of student’s t-test, analysis of variance, and covariance. Annals of Cardiac Anaesthesia, 22(4), 407–411. https://doi.org/10.4103/aca.ACA-94-19
  17. Muhammad Azhar Khairi, Tb Ai Munandar, S. S. (2021). Journal of dinda. 1(2), 82–87
  18. Musdholifah, A. (2018). Intisari deteksi sarkasme untuk analisis sentimen pada. 4–5
  19. Nurdin, A., Aji, B. A. S., Bustamin, A., & Abidin, Z. (2020). Perbandingan Kinerja Word Embedding Word2Vec , Glove ,. Jurnal TEKNOKOMPAK, 14(2), 74–79
  20. Romli, M., Kamarula, F., & Rochmawati, N. (2022). Perbandingan CNN dan Bi-Lstm pada Analisis Sentimen dan Emosi Masyarakat Indonesia Di Media Sosial Twitter Selama Pandemik Covid-19 yang Menggunakan Metode. 04, 219–228
  21. Samsir, S., Ambiyar, A., Verawardina, U., Edi, F., & Watrianthos, R. (2021). Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes. JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(1), 157. https://doi.org/10.30865/mib.v5i1.2580
  22. Saragih, L., Nababan, M., Simatupang, Y., & Amalia, J. (2022). Analisis Self-Attention Pada Bi-Directional Lstm Dengan Fasttext Dalam Mendeteksi Emosi Berdasarkan Text. ZONAsi: Jurnal Sistem Informasi, 4(2), 144–156. https://doi.org/10.31849/zn.v4i2.10846
  23. Sharma, S., Sharma, S., & Anidhya, A. (2020). Understanding Activation Functions in Neural Networks. International Journal of Engineering Applied Sciences and Technology, 4(12), 310–316
  24. Syaliman, K. U., & Riau, P. C. (2022). Peringkasan dokumen teks otomatis berdasarkan sebuah kueri menggunakan bidirectional long short term memory network automatic text document summary based on a query using bidirectional long short term memory network. 5
  25. Utami, D., & Putri, K. (2021). Analisis Sentimen dengan Deteksi Sarkasme pada Komentar Instagram Politikus AISYAH MUHADDISI, Drs. Bambang Nurcahyo Prastowo, M. Sc.; Diyah Utami Kusumaning Putri, S.Kom., M.Sc., M. 70–71
  26. Zalmout, N., & Habash, N. (2020). Utilizing Subword Entities in Character-Level Sequence-to-Sequence Lemmatization Models. COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference, 4676–4682. https://doi.org/10.18653/v1/2020.coling-main.412

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