Leveraging Machine Translation to Enhance Sentiment Analysis on Multilingual Text
Pages 242 - 248
Abstract
This research investigates the complexity of sentiment analysis in the multilingual context of Malaysia, where various languages, including English and Malay, are often intermixed. Since existing sentiment analysis tools are predominantly cater for single-language scenarios only, it results in creating a gap for effective analysis in this diverse linguistic environment. To bridge this gap, a comprehensive solution utilizing Machine Translation (MT) to homogenize the language in code-mixed texts is experimented in this study. Several approaches to sentiment analysis were utilized to showcase the feasibility of MT in enhancing sentiment analysis. By translating code-mixed text into its equivalent text in a uniform language accurately, a significant improvement in sentiment analysis were observed, indicating the contribution of this work in surpassing language barriers in multilingual sentiment analysis.
References
[1]
Basant Agarwal, Namita Mittal, Pooja Bansal, and Sonal Garg, “Sentiment Analysis Using Common-Sense and Context Information,” Computational Intelligence and Neuroscience, pp. 1–9. 2015. https://doi.org/10.1155/2015/715730
[2]
Majlinda Axhiu, Florida Veljanoska, Biljana Ciglovska, and Mirlinda Husejni, “The Usage of Sentiment Analysis for Hearing the Voice of the Customer and Improving Businesses,” Journal of Educational and Social Research, Vol. 4, No. 4, pp. 401–407, June 2014.
[3]
Kia Dashtipour, Soujanya Poria, Amir Hussain, Erik Cambria, Ahmad Y A Hawalah, Alexander Gelbukh, and Qiang Zhou, “Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques,” Cognitive Computation, Vol. 8, No. 4, pp. 757–771, June 2016.
[4]
Nanda Putri Romadhona, Sin-En Lu, Bo-Han Lu, and Richard Tzong-Han Tsai, “BRCC and SentiBahasaRojak: The First Bahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset,” pp. 4418–4428, October 2022. https://aclanthology.org/2022.coling-1.389.pdf.
[5]
Nor Asni Syahriza Abu Hassan, “Bahasa rojak: Malaysian way of speaking English?,” KONAKA Konferensi Akademik 2015, pp. 234-239, November 2015. https://ir.uitm.edu.my/id/eprint/68094/2/68094.pdf
[6]
Muhammad Irfan, “Machine Translation,” October 2017. https://www.researchgate.net/publication/320730405
[7]
Grace Hui-Chin Lin and Paul Shih Chieh Chien, “Machine Translation for Academic Purposes,” Proceedings of the International Conference on TESOL and Translation 2009, pp. 133–148, December 2009. https://files.eric.ed.gov/fulltext/ED513879.pdf
[8]
Haifeng Wang, Hua Wu, Zhongjun He, Liang Huang, and Kenneth Ward Church, “Progress in Machine Translation,” Engineering, Vol. 18, pp. 143–153. November 2022. https://doi.org/10.1016/j.eng.2021.03.023
[9]
Emily Ohman, “The Validity of Lexicon-based Emotion Analysis in Interdisciplinary Research,” Proceedings of the Workshop on Natural Language Processing for Digital Humanities, pp. 7–12, December 2021. https://doi.org/10.26615/978-952-94-5833-2_002
[10]
Manuel Ojeda-Hernández, Domingo López-Rodríguez, and Ángel Mora, “Lexicon-based sentiment analysis in texts using Formal Concept Analysis,” International Journal of Approximate Reasoning, Vol. 155, pp. 104–112, April 2023. https://doi.org/10.1016/j.ijar.2023.02.001.
[11]
TextBlob: Simplified Text Processing. https://textblob.readthedocs.io/en/dev/
[12]
Wan Nur Syahirah Wan Min and Nur Zareen Zulkarnain, “Comparative Evaluation of Lexicons in Performing Sentiment Analysis,” Journal of Advanced Computing Technology and Application (JACTA), Vol. 2, No. 1, May 2020.
[13]
Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani, “SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining,” Proceedings of the 8th International Conference on Language Resources and Evaluation, pp. 2201–2204, May 2010. https://doi.org/10.1145/3227609.3227664
[14]
C.J. Hutto and Eric Gilbert, “VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text,” Proceedings of the International AAAI Conference on Web and Social Media, Vol. 8, No. 1, pp. 216–225.
[15]
Yi-Fei Tan, H. Lam, Asyraf Azlan, and Wooi-King Soo, “Sentiment analysis for telco popularity on Twitter big data using a novel Malaysian dictionary,” Frontiers in Artificial Intelligence and Applications, Vol. 282, pp. 112–125, 2016.
[16]
Khalifa Chekima and Rayner Alfred, “Sentiment Analysis of Malay Social Media Text,” Proceedings of International Conference on Computational Science and Technology, pp. 205–219, February 2018.
[17]
Nurul Husna Mahadzir, Mohd Faizal Omar, Mohd Nasrun Mohd Nawi, Anas A. Salameh, Kasmaruddin Che Hussin, and Abid Sohail, “MELex: The Construction of Malay-English Sentiment Lexicon,” Computers, Materials & Continua, Vol. 71, No. 1, pp. 1789–1805, 2022.
[18]
Mesolitica, November 2023. https://github.com/mesolitica/malaysian-dataset/
[19]
Saif Mohammad, “A Practical Guide to Sentiment Annotation: Challenges and Solutions,” Association for Computational Linguistics, pp. 174–179, June 2016.
[20]
MK Zainal, “Eh what does ‘xtau’ mean?” – A dictionary of Malay SMS short-forms for your sanity. January 2016. https://cilisos.my/bahasa-sms-shortforms-glossary/
[21]
Googletrans: Free and Unlimited Google translate API for Python. https://py-googletrans.readthedocs.io/en/latest/.
[22]
Lu Xing Han, “DL Translate: A Deep Learning-based Translation Library Built on Huggingface Transformers,” 2021.
[23]
Nisheeth Joshi, Iti Mathur, Hemant Darbari, and Ajai Kumar, “HEVAL: Yet Another Human Evaluation Metric,” International Journal on Natural Language Computing (IJNLC), Vol. 2, No.5, pp. 21–36, November 2013.
[24]
Abdul Mohaimin Rahat, Abdul Kahir, and Abu Kaisar Mohammad Masum, “Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset,” Proceedings of the 8th International Conference System Modeling and Advancement in Research Trends (SMART), pp. 266–270, November 2019.
[25]
Julius Sim and Chris C Wright, “The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements,” Physical Therapy, Vol. 85, No. 3, pp. 257–268, March 2005. https://doi.org/10.1093/ptj/85.3.257
[26]
Harry N. Boone Jr and Deborah A. Boone, “Analyzing Likert Data,” The Journal of Extension, Vol. 50, Issue 2, April 2012.
[27]
Thomas Schmidt, Brigitte Winterl, Milena Maul, Alina Schark, Andrea Vlad and Christian Wolff, “Inter-Rater Agreement and Usability: A Comparative Evaluation of Annotation Tools for Sentiment Annotation,” INFORMATIK 2019 Workshops, pp. 121–133, September 2019.
Recommendations
An evaluation of machine translation for multilingual sentence-level sentiment analysis
SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied ComputingSentiment analysis has become a key tool for several social media applications, including analysis of user's opinions about products and services, support to politics during campaigns and even for market trending. There are multiple existing sentiment ...
Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis
Special issue on Deep Learning for Low-Resource Natural Language Processing, Part 1 and Regular PapersA novel Indonesian sentiment lexicon (SentIL -- Sentiment Indonesian Lexicon) is created with an automatic pipeline; from creating sentiment seed words, adding new words with slang words, emoticons, and from the given dictionary and sentiment corpus, ...
Sentiment Analysis of Code-Mixed Languages Leveraging Resource Rich Languages
Computational Linguistics and Intelligent Text ProcessingAbstractCode-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages.
In this paper, we propose a novel approach called Sentiment Analysis of Code-...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
February 2024
395 pages
ISBN:9798400708329
DOI:10.1145/3651781
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICSCA 2024
ICSCA 2024: 2024 13th International Conference on Software and Computer Applications
February 1 - 3, 2024
Bali Island, Indonesia
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 382Total Downloads
- Downloads (Last 12 months)382
- Downloads (Last 6 weeks)66
Reflects downloads up to 16 Feb 2025
Other Metrics
Citations
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatLogin options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in