Abstract
Sentiment analysis refers to the use of natural language processing (NLP) and textual analysis approaches to identify and extract subjective information from textual sources. Extracting sensible financial knowledge from relevant textual material is significant in order to help leverage the predictive power of the financial and econometric forecasting models. However, the determination of the sentiment analysis from textual data such as headlines, news and user comments is not an easy task. One of the most arduous challenges when dealing with sentiment analysis is the accuracy. In this work, a new lexicon-based approach is presented which is based on supervised learning. The introduced model is able to create a new lexicon based on annotated textual data and then it applies that lexicon to determine the sentiment in new, not-annotated data. The proposed method seems able to work effectively with financial data while supporting accurate decisions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mantyla, M.V., Graziotin, M., Kuutila, M.: The evolution of sentiment analysis - a review of research topics, venues, and top cited papers. arXiv preprint arXiv:1612.01556
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37, 267–307 (2011)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project report, Stanford, p. 12 (2009)
Obaidat, M.S., Papadimitriou, G.I., Pomportsis, A.S.: Learning automata: theory, paradigms, and applications. IEEE Trans. Syst. Man Cybern. Part B 32, 706–709 (2002)
Nicopolitidis, P., Papadimitriou, G.I., Sarigiannidis, P., Obaidat, O., Pomportsis, A.S.: Adaptive wireless networks using learning automata. IEEE Wirel. Commun. 18, 75–81 (2011)
Sarigiannidis, A.G., Nicopolitidis, P., Papadimitriou, G.I., Sarigiannidis, P., Louta, M.D., Pomportsis, A.S.: On the use of learning automata in tuning the channel split ratio of WiMAX networks. IEEE Syst. J. 18, 651–663 (2015)
Fahimi, M., Ghasemi, A.: A distributed learning automata scheme for spectrum management in self-organized cognitive radio network. IEEE Trans. Mob. Comput. 16, 1490–1501 (2017)
Sohrabi, M.K., Roshani, R.: Frequent itemset mining using cellular learning automata. Comput. Hum. Behav. 68, 244–253 (2017)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37, 244–253 (2011)
Loughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10-ks. J. Financ. 66, 35–65 (2011)
Avanco, L.V., das Gracas Volpe Nunes, M.: Lexicon-based sentiment analysis for reviews of products in Brazilian Portuguese. In: Brazilian Conference on Intelligent Systems, pp. 35–65 (2014)
Bandhakavi, A., Wiratunga, N., Massie, S., Deepak, P.: Emotion-corpus guided lexicons for sentiment analysis on Twitter. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXXIII, pp. 71–85. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47175-4_5
Ghavipour, M., Meybodi, M.R.: Trust propagation algorithm based on learning automata for inferring local trust in online social networks. Knowl.-Based Syst. 143, 307–316 (2017)
http://projectprofit.eu/ (2016)
Ku, L.W., Lo, Y.S., Chen, H.H.: Test collection selection and gold standard generation for a multiply-annotated opinion corpus. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 89–92 (2007)
Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 39, 165–210 (2005)
https://nlp.stanford.edu/ (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Sarigiannidis, A., Karypidis, PA., Sarigiannidis, P., Pragidis, IC. (2018). A Novel Lexicon-Based Approach in Determining Sentiment in Financial Data Using Learning Automata. In: Diplaris, S., Satsiou, A., Følstad, A., Vafopoulos, M., Vilarinho, T. (eds) Internet Science. INSCI 2017. Lecture Notes in Computer Science(), vol 10750. Springer, Cham. https://doi.org/10.1007/978-3-319-77547-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-77547-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77546-3
Online ISBN: 978-3-319-77547-0
eBook Packages: Computer ScienceComputer Science (R0)