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Sentiment Analysis of Portuguese Economic News

Authors Cátia Tavares, Ricardo Ribeiro , Fernando Batista

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Author Details

Cátia Tavares
  • Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa, Portugal
Ricardo Ribeiro
  • Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa, Portugal
  • INESC-ID, Lisbon, Portugal
Fernando Batista
  • Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa, Portugal
  • INESC-ID, Lisbon, Portugal

Funding

This work was supported by national funds through FCT, Fundação para a Ciência e a Tecnologia, under project UIDB/50021/2020.

Cite As Get BibTex

Cátia Tavares, Ricardo Ribeiro, and Fernando Batista. Sentiment Analysis of Portuguese Economic News. In 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Open Access Series in Informatics (OASIcs), Volume 94, pp. 17:1-17:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/OASIcs.SLATE.2021.17

Abstract

This paper proposes a rule-based method for automatic polarity detection over economic news texts, which proved suitable for detecting the sentiment in Portuguese economic news. The data used in our experiments consists of 400 manually annotated sentences extracted from economic news, used for evaluation, and about 90 thousand Portuguese economic news, extracted from two well-known Portuguese newspapers, covering the period from 2010 to 2020, that have been used for training our systems. In order to perform sentiment analysis of economic news, we have also tested the adaptation of existing pre-trained modules, and also performed experiments with a set of Machine Learning approaches, and self-training. Experimental results show that our rule-based approach, that uses manually written rules related to the economic context, achieves the best results for automatically detecting the polarity of economic news, largely surpassing the other approaches.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning
  • Information systems → Language models
  • Computing methodologies → Natural language processing
  • Applied computing → Economics
  • Computing methodologies → Language resources
  • Computing methodologies → Artificial intelligence
  • General and reference → Measurement
Keywords
  • Sentiment Analysis
  • Economic News
  • Portuguese Language

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References

  1. Pablo Aguilar, Corinna Ghirelli, Matías Pacce, and Alberto Urtasun. Can News Help Measure Economic Sentiment? An Application in COVID-19 Times. SSRN Electronic Journal, 2020. URL: https://doi.org/10.2139/ssrn.3673825.
  2. Jeelani Ahmed and Muqeem Ahmed. A framework for sentiment analysis of online news articles. International Journal on Emerging Technologies, 11(3):267-274, 2020. Google Scholar
  3. Alexandra Balahur, Ralf Steinberger, Mijail Kabadjov, Vanni Zavarella, Erik Van Der Goot, Matina Halkia, Bruno Pouliquen, and Jenya Belyaeva. Sentiment analysis in the news. In Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC 2010, pages 2216-2220. ELRA, 2010. URL: http://arxiv.org/abs/1309.6202.
  4. Ivan Boban, Alen Doko, and Sven Gotovac. Sentence retrieval using Stemming and Lemmatization with different length of the queries. Advances in Science, Technology and Engineering Systems, 5(3):349-354, 2020. URL: https://doi.org/10.25046/aj050345.
  5. Clément Bortoli, Stéphanie Combes, and Thomas Renault. Nowcasting GDP Growth by Reading the Newspapers. Economie et Statistique / Economics and Statistics, 505-506:17-33, 2018. URL: https://EconPapers.repec.org/RePEc:nse:ecosta:ecostat_2018_505-506_2.
  6. Charles W. Calomiris and Harry Mamaysky. How news and its context drive risk and returns around the world. Journal of Financial Economics, 133(2):299-336, 2019. URL: https://doi.org/10.1016/j.jfineco.2018.11.009.
  7. Cagatay Catal and Mehmet Nangir. A sentiment classification model based on multiple classifiers. Applied Soft Computing Journal, 50:135-141, 2017. URL: https://doi.org/10.1016/j.asoc.2016.11.022.
  8. Alyt Damstra and Mark Boukes. The Economy, the News, and the Public: A Longitudinal Study of the Impact of Economic News on Economic Evaluations and Expectations. Communication Research, page 009365021775097, 2018. URL: https://doi.org/10.1177/0093650217750971.
  9. Ronen Feldman. Techniques and applications for sentiment analysis. Commun. ACM, 56(4):82–89, 2013. URL: https://doi.org/10.1145/2436256.2436274.
  10. Samuel P. Fraiberger. News Sentiment and Cross-Country Fluctuations. SSRN Electronic Journal, pages 1-18, 2016. URL: https://doi.org/10.2139/ssrn.2730429.
  11. Ippei Fujiwara, Yasuo Hirose, and Mototsugu Shintani. Can News Be a Major Source of Aggregate Fluctuations? A Bayesian DSGE Approach. Journal of Money, Credit and Banking, 43(1):1-29, 2011. URL: https://doi.org/10.1111/j.1538-4616.2010.00363.x.
  12. Elena Georgiadou, Spyros Angelopoulos, and Helen Drake. Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes. International Journal of Information Management, 51(November):102048, 2020. URL: https://doi.org/10.1016/j.ijinfomgt.2019.102048.
  13. Joe Bob Hester and Rhonda Gibson. The economy and second-level agenda setting: A time-series analysis of econom... Journalism & Mass Communication Quarterly, 80(1), 2003. Google Scholar
  14. Chengyu Huang, Sean Simpson, Daria Ulybina, and Agustin Roitman. News-based Sentiment Indicators. IMF Working Papers, 19(273), 2019. URL: https://doi.org/10.5089/9781513518374.001.
  15. Clayton J. Hutto and Eric Gilbert. VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Eytan Adar, Paul Resnick, Munmun De Choudhury, Bernie Hogan, and Alice H. Oh, editors, Proceedings of the Eighth International Conference on Weblogs and Social Media, ICWSM 2014, Ann Arbor, Michigan, USA, June 1-4, 2014. The AAAI Press, 2014. URL: http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109.
  16. Samina Khalid, Tehmina Khalil, and Shamila Nasreen. A survey of feature selection and feature extraction techniques in machine learning. Proceedings of 2014 Science and Information Conference, SAI 2014, pages 372-378, 2014. URL: https://doi.org/10.1109/SAI.2014.6918213.
  17. Anuj Mahajan, Lipika Dey, and Sk Mirajul Haque. Mining financial news for major events and their impacts on the market. In Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008, pages 423-426, 2008. URL: https://doi.org/10.1109/WIIAT.2008.309.
  18. Walaa Medhat, Ahmed Hassan, and Hoda Korashy. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4):1093-1113, 2014. URL: https://doi.org/10.1016/j.asej.2014.04.011.
  19. Ayman Mohamed. An Evaluation of Sentiment Analysis and Classification Algorithms for Arabic Textual Data. International Journal of Computer Applications, 158(3):29-36, 2017. URL: https://doi.org/10.5120/ijca2017912770.
  20. Hamed Naderi Semiromi, Stefan Lessmann, and Wiebke Peters. News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar. North American Journal of Economics and Finance, 52(December 2018):101181, 2020. URL: https://doi.org/10.1016/j.najef.2020.101181.
  21. Rickard Nyman, Sujit Kapadia, and David Tuckett. News and narratives in financial systems: exploiting big data for systemic risk assessment. Journal of Economic Dynamics and Control, 2021. URL: https://doi.org/10.1016/j.jedc.2021.104119.
  22. Nataliia Ostapenko. Macroeconomic Expectations: News Sentiment Analysis. Working Paper Series. Eesti Pank, 2020. URL: https://doi.org/10.23656/25045520/052020/0178.
  23. Denilson Alves Pereira. A survey of sentiment analysis in the Portuguese language. Artificial Intelligence Review, 54(2):1087-1115, 2020. URL: https://doi.org/10.1007/s10462-020-09870-1.
  24. Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. Stanza: A Python natural language processing toolkit for many human languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2020. URL: https://nlp.stanford.edu/pubs/qi2020stanza.pdf.
  25. Sahar Sohangir, Nicholas Petty, and DIngding Wang. Financial Sentiment Lexicon Analysis. In Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018, volume 2018-January, pages 286-289. Institute of Electrical and Electronics Engineers Inc., April 2018. URL: https://doi.org/10.1109/ICSC.2018.00052.
  26. Michael Stanger. A Monthly Indicator of Economic Growth for Low Income Countries. IMF Working Papers, 20(13), 2020. URL: https://doi.org/10.5089/9781513525853.001.
  27. Paul C. Tetlock. Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3):1139-1168, 2007. URL: https://doi.org/10.1111/j.1540-6261.2007.01232.x.
  28. Leif Anders Thorsrud. Nowcasting Using News Topics. Big Data versus Big Bank. In Norges Bank Research, Working papers from Norges Bank. Norges Bank, 2017. URL: https://doi.org/10.2139/ssrn.2901450.
  29. Yenny Villuendas-Rey, Carmen F. Rey-Benguría, Ángel Ferreira-Santiago, Oscar Camacho-Nieto, and Cornelio Yáñez-Márquez. The Naïve Associative Classifier (NAC): A novel, simple, transparent, and accurate classification model evaluated on financial data. Neurocomputing, 265:105-115, 2017. URL: https://doi.org/10.1016/j.neucom.2017.03.085.
  30. Ritu Yadav, A. Vinay Kumar, and Ashwani Kumar. News-based supervised sentiment analysis for prediction of futures buying behaviour. IIMB Management Review, 31(2):157-166, 2019. URL: https://doi.org/10.1016/j.iimb.2019.03.006.
  31. Shanshan Yi and Xiaofang Liu. Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers' review. Complex & Intelligent Systems, 6(3):621-634, 2020. URL: https://doi.org/10.1007/s40747-020-00155-2.
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