Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models
DOI:
https://doi.org/10.31449/inf.v47i5.4673Abstract
Recently, extensive research in the field of financial sentiment analysis has been conducted. Sentiment analysis (SA) of any text data denotes the feelings and attitudes of the individual on particular topics or products. It applies statistical approaches with artificial intelligence (AI) algorithms to extract substantial knowledge from a huge amount of data. This study extracts the Sentiment polarity (negative, positive, and neutral) from financial textual data using machine learning and deep learning algorithms. The constructed machine learning model used Multinomial Naïve Bayes (MNB) and Logistic regression (LR) classifiers. On the other hand, three deep learning algorithms have been utilized which are Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results of the MNB and LR are obtained good and very good rate of accuracy respectively. Likewise, the results of RNN, LSTM and GRU obtained an excellent rate of accuracy. It can be concluded from the outcomes that the used preprocessing stages made a positive impact on the accuracy rate.References
DAVID ZIMBRA, AHMED ABBASI, Daniel Zeng, Hsinchun Chen, "The State-of-the-Art in Twitter Sentiment Analysis: A Review and Benchmark Evaluation," ACM Transactions on Management Information Systems, vol. 9, no. 2, p. 1–29, June 2018.
"Review on sentiment analysis for text classification techniques from 2010 to 2021," Multimedia Tools and Applications, pp. 2-58, October 2022.
Sensen Guo, Xiaoyu Li, Zhiying Mu, "Adversarial Machine Learning on Social Network: A Survey," Frontiers in Physics, vol. 9, pp. 1-18, November 2021.
Mayur Wankhade, Annavarapu Chandra Sekhara Rao, Chaitanya Kulkarni, "A survey on sentiment analysis methods, applications, and challenges," Artifcial Intelligence Review, vol. 55, p. 5731–5780, February 2021.
Yili Wang, Jiaxuan Guo , Chengsheng Yuan, Baozhu Li, "Sentiment Analysis of Twitter Data," Applied Sciences MDPI, pp. 2-14, November 2022.
Zenun Kastrati, Fisnik Dalipi, Ali Shariq Imran, Krenare Pireva Nuci, Mudasir Ahmad Wani, "Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study," Applied Sciences MDPI, vol. 11, pp. 1- 23, April 2021.
Rokas Štrimaitis 1, Pavel Stefanovi, Simona Ramanauskaite, Asta Slotkiene, "Financial Context News Sentiment Analysis for the Lithuanian Language," Applied Sciences MDPI, vol. 11, pp. 1 - 13, May 2021.
ALI SHARIQ IMRAN, SHER MUHAMMAD DAUDPOTA, ZENUN KASTRATI, RAKHI BATRA, "Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets," IEEE Access, vol. 8, pp. 181074 - 181090, September 2020.
Mohammed Nazim Uddin, Md. Ferdous Bin Hafiz, Sohrab Hossain, Shah Mohammad Mominul Islam, "Drug Sentiment Analysis using Machine Learning Classifiers," (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 13, no. 1, pp. 92- 100, 2022.
Duyu Tang, Bing Qin, Ting Liu, "Deep learning for sentiment analysis: successful approaches and future challenges," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 5, p. 292–303, October 2015.
Fatemeh Hemmatian, Mohammad Karim Sohrabi, "A survey on classification techniques for opinion mining and sentiment analysis," Artif Intell Rev, vol. 52, p. 1495–1545, 2019.
Osama M. Rababah, Ahmad K. Hwaitat, Dana A. Al Qudah, "Sentiment analysis as a way of web optimization," Scientific Research and Essays, vol. 11, no. 8, pp. 89 - 96, April 2016.
Anastasia Giachanou, Fabio Crestani,, "Like It or Not: A Survey of Twitter Sentiment Analysis Methods," ACM Computing Surveys, vol. 49, no. 2, p. 1–41, June 2017.
Golam Mostafa, Ikhtiar Ahmed, Masum Shah Junayed, "Investigation of Different Machine Learning Algorithms to Determine Human Sentiment Using Twitter Data," I.J. Information Technology and Computer Science, vol. 2, pp. 38-48, 2021.
Arwa A. Al Shamsi, Sherief Abdallah, "Sentiment Analysis of Emirati Dialects," Big Data and Cognitive Computing, Vols. 6-57, pp. 1-18, May 2022.
ALI FEIZOLLAH, SULAIMAN AININ, NOR BADRUL ANUAR, "Halal Products on Twitter: Data Extraction and Sentiment Analysis Using Stack of Deep Learning Algorithms," IEEE Access, Vols. 7, 2019, pp. 83354-83362, June 17, 2019.
Bledar Fazlija * and Pedro Harder, "Using Financial News Sentiment for Stock Price," MDPI Mathematics, pp. 1-20, June 2022.
Hassan Fouadi, Hicham El Moubtahij, Hicham Lamtougui, Ali Yahyaouy, "Sentiment Analysis of Arabic Comments Using Machine Learning and Deep Learning Model," Indian Journal of Computer Science and Engineering (IJCSE), vol. 13, no. 3, pp. 598 - 606, June 2022.
HARISU ABDULLAHI SHEHU, MD. HAIDAR SHARIF, MD. HARIS UDDIN SHARIF, RIPON DATTA, SEZAI TOKAT, SAHIN UYAVER, HUSEYIN KUSETOGULLARI, RABIE A. RAMADAN, "Deep Sentiment Analysis: A Case Study on Stemmed Turkish Twitter Data," IEEE Access, vol. 9, pp. 56836 - 56854, April 2021.
S. Liu, "Sentiment Analysis of Yelp Reviews: A Comparison of Techniques and Models abs/2004.13851 (2020): n. pag.," ArXiv, April 2020.
Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika
