Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods

Authors

  • Jelita Asian School of Computer Science, Nusa Putra University, Indonesia, Indonesia
  • Moneyta Dholah Rosita School of Computer Science, Nusa Putra University, Indonesia
  • Teddy Mantoro Media-Tech Lab, Department of Computer Science, Sampoerna University, Indonesia

DOI:

https://doi.org/10.15575/join.v7i1.900

Keywords:

sentiment analysis, random forest, Multi-Layer Perceptron Classifier, twitter, sexual harassment

Abstract

Sexual harassment is defined as giving sexual attention both verbally, either in speech or writing, and physically to victims who are predominantly women, On July 13, 2022, there was a tweet featuring a video of sexual harassment that made it trend in various countries. The video irritated Twitter users and made various comments resulting in various sentiments that can be analyzed using sentiment analysis. The purpose of this study is to see what the public thinks about the sexual harassment case of Brazilian anesthesiologist. Besides the sentiment analysis, another aim of this study is to see how objective are those sentiments based on their polarity. This study uses a comparison of two methods in sentiment analysis, namely Multi-Layer Perceptron Classifier and Random Forest, and labeling automatically using TextBlob.  This results in 94.44% accuracy, 94.44% precision, 92% recall and 93% f1_score. For MLP Classifier and accuracy 96.42%, precision 94.44%, recall 96.66% and f1_score 95.56% for Random Forest. Sentiment polarity score from the TextBlob is -0.5 and subjectivity is 0.4 which indicates that most statements are negative and subjective score is 0.4, which means those sentiments are subjective in nature.

References

L. A. Rudman, E. Borgida & B. A. Robertson. Suffering in silence: Procedural justice versus gender socialization issues in university sexual harassment grievance procedures. Basic and Applied Social Psychology, 17(4), 519–541. 1995. https://doi.org/10.1207/s15324834basp1704_6

T. Luo, S. Chen, G. Xu, J. Zhou, Sentiment Analysis. In: Trust-based Collective View Prediction. Springer, New York, NY. 2013,https://doi.org/10.1007/978-1-4614-7202-5_4

D. A. Alboaneen, H. Tianfield and Y. Zhang, 2017, Sentiment analysis via multi-layer perceptron trained by meta-heuristic optimisation 2017 IEEE International Conference on Big Data (Big Data), pp. 4630-4635, doi: 10.1109/BigData.2017.8258507

Jyoti Budhwar, Sukhdip Singh, 2021, Sentiment Analysis based Method for Amazon Product Reviews, International Journal Of Engineering Research & Technology (Ijert) Icact – 2021 (Volume 09 – Issue 08)

J. Kusuma, B. Herawan, W. Wanayumini and R. Rosnelly “Komparasi Metode Multi Layer Perceptron (MLP) dan Support Vector Machine (SVM) untuk Klasifikasi Kanker Payudara Komparasi Metode Multi Layer Perceptron (MLP) dan Support Vector Machine (SVM) untuk Klasifikasi Kanker Payudara†2022 in Multimedia Artificial Intelligence Networking Database 7:1, 51-60, June 2022.MIND, vol.7 no 1 pp 51 – 60, Juni 2022.

Dashtipour, K., Gogate, M., Adeel, A., Ieracitano, C., Larijani, H., Hussain, A. (2018). Exploiting Deep Learning for Persian Sentiment Analysis. In: , et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science, vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_58.

D.R. Vivas, M. Javier, B. Victor, O. L. G'omez and J. H. Reina. “Neural-Network Quantum States: A Systematic Review.†2022.

A. Maher & Hussain, Suhail & Ustun, T. Selim & Sarker, Mahidur & Hannan, M. A. & Mohamed, Ramizi & Ali, Jamal & Mekhilef, Saad & Milad, Abdalrhman. Artificial Neural Networks Based Optimization Techniques: A Review. Electronics. 10. 2689. 10.3390/electronics10212689. 2021

S. Saifullah, Y. Fauziah, and A. S. Aribowo (2021), "Comparison of Machine Learning for Sentiment Analysis in Detecting Anxiety Based on Social Media Data," DOI: 10.26555/jifo.v15i1.a20111.

G. Biau and E. Scornet. A random forest guided tour. Test, 25(2):197–227, 2016.

K. Budiman, N. Zaatsiyah, U. Niswah, and F. Faizi, “Analysis of Sexual Harassment Tweet Sentiment on Twitter in Indonesia using Naive Bayes Method through National Institute of Standard and Technology Digital Forensic Acquisition Approachâ€, JAIST, vol. 2, no. 2, pp. 21-30, Oct. 2020.

Y. Al Amrani, M. Lazaar, K.E. El Kadiri, Random forest and support vector machine based hybrid approach to sentiment analysis Procedia Comput. Sci., 127 (2018), pp. 511-520

Mantoro, M. Merdianti and M. A. Ayu, "Sentiment Analysis of the Papuan Movement on Twitter Using Naïve Bayes Algorithm," 2021 IEEE 7th International Conference on Computing, Engineering and Design (ICCED), Sukabumi, Indonesia, 2021, pp. 1-5. doi: 10.1109/ICCED53389.2021.9664868.

T. Mantoro, M. A. Ayu, R. T. Handayanto. Machine Learning Approach for Sentiment Analysis in Crime Information Retrieval, 3rd International Conference of Computer And Informatics Engineering IC2IE 2020, Jakarta, 15-16 Sept 2020.

B. Balachander, P. S. J. Sentimental Analysis of Twitter Data Using Tweepy and Textblob. International Journal of Advanced Science and Technology, 2020 29(3), 6537 - 6544. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/7243

Z. Drus and H. Khalid, “Sentiment analysis in social media and its application: Systematic literature review,†Procedia Comput. Sci., vol.161, pp. 707–714, 2019, doi: 10.1016/j.procs.2019.11.174.

C.D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. Retrieved from https://nlp.stanford.edu/IR-book/information-retrieval-book.html.

Bahrawi. Sentiment Analysis Using Random Forest Algorithm Online Social Media Based. Journal Of Information Technology and Its Utilization, Volume 2, Issue 2, December-2019: 29-332. h.29-33..

V. A. Fitri, R. Andreswari and M. A. Hasibuan, "Sentiment analysis of social media Twitter with case of AntiLGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm," Procedia Computer Science, vol. 161, pp. 765-772, 2019.

R. Sari and R. Y. Hayuningtyas, “Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Pada Wisata TMII Berbasis Website†Indones. J. Softw. Eng., vol. 5, no. 2, pp. 51–60, 2019, doi: 10.31294/ijse.v5i2.6957.

I. G. S. Mas Diyasa, N. M. I. Marini Mandenni, M. I. Fachrurrozi, S. I. Pradika, K. R. Nur Manab and N. R. Sasmita, "Twitter Sentiment Analysis as an Evaluation and Service Base On Python Textblob", IOP Conf. Ser. Mater. Sci. Eng., vol. 1125, no. 1, pp. 012034, 2021.

M.A. Ayu, S.S. Wijaya, T. Mantoro, An automatic lexicon generation for Indonesian news sentiment analysis: A case on governor elections in Indonesia, Indonesian Journal of Electrical Engineering and Computer Science, 16 (3), pp. 1555-1561, 2019.

R. Kurniasari, R. Santoso, and A. Prahutama, "Analisis Kecenderungan Laporan Masyarakat Pada “LAPORGUB..!†Provinsi Jawa Tengah Menggunakan Text Mining Dengan Fuzzy C-Means Clustering," Jurnal Gaussian, vol. 10, no. 4, pp. 544-553, Dec. 2021. https://doi.org/10.14710/j.gauss.v10i4.33101.

S. S. Chai, W. L. Cheah, K. L. Goh, Y. H. R. Chang, K. Y. Sim, K. O. Chin, "A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia", Computational and Mathematical Methods in Medicine, vol. 2021, Article ID 2794888, 11 pages, 2021. https://doi.org/10.1155/2021/2794888.

F. Heimerl, S. Lohmann, S. Lange and T. Ertl, "Word Cloud Explorer: Text Analytics Based on Word Clouds," 2014 47th Hawaii International Conference on System Sciences, 2014, pp. 1833-1842, doi: 10.1109/HICSS.2014.231.

K. Khadijah and R. Kusumaningrum. “Ensemble Classifier untuk Klasifikasi Kanker Payudara.†IT JOURNAL RESEARCH AND DEVELOPMENT, 2019, https://doi.org/10.25299/itjrd.2019.vol4(1).3540.

A. Setiawan., I.G. S. M. Diyasa, M. Hatta, and E. Y. Puspaningrum. “Mixture gaussian V2 based microscopic movement detection of human spermatozoa†International Journal of Advances in Intelligent Informatics, Vol. 6 (2), pp. 210-222, 2020.

I. Sunni dan D. H. Widyantoro, "Analisis Sentimen Dan Ekstraksi Topik Penentu Sentimen Pada Opini Terhadap Tokoh Publik" Jurnal Sarjana Institut Teknologi Bandung Bidang Teknik Elektro dan Informatika, vol. 1, no. 2, pp. 200-206, Jul. 2022.

A. Primajaya and B. N. Sari, "Random Forest Algorithm for Prediction of Precipitation," Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), vol. 1, no. 1, pp. 27-31, 2018.

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Published

2022-09-12

Issue

Vol. 7 No. 1 (2022)

Section

Article

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