Abstract
The aim of the chapter is to present the conception of the prediction module for supporting investment decisions based on the cognitive technology. Managers must make investment decisions that are very important for a company’s development. Decisions are made based on scenario analysis. Preparation of these scenarios is based on data and information from sources from the company and its environment. An important source of data are social media, which can contain valuable information, but also rubbish. The problem is to extract from a huge amount of data in social media information which is valuable and useful for a company in forecasting future situations. The chapter contains conclusions from the study on the use of cognitive technology in obtaining valuable information from Twitter to forecast investment scenarios. We have discussed the proposal of the prognostic investment decision supporting module and present a case study analysis that refers to the use of cognitive technologies in the this module to support investment decision making by managers in small and medium-sized enterprises. The contribution of this chapter is a proposal of the prediction module for supporting investment decisions based on the cognitive technology.
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The network is powered by learning data, which is saved in the form of semantic graphs. The agent searches the texts for sentences containing common words because it determines the degree of similarity that directly affects the evaluation. If a sentence has many connections, the system classifies it as relevant.
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Acknowledgements
The project is financed by the Ministry of Science and Higher Education in Poland under the program “Regional Initiative of Excellence” 2019—2022 project number 015/RID/2018/19 total funding amount 10 721 040,00 PLN.
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Oleksyk, P., Hernes, M., Nita, B., Dudycz, H., Kozina, A., Janus, J. (2020). Supporting Investment Decisions Based on Cognitive Technology. In: Hernes, M., Rot, A., Jelonek, D. (eds) Towards Industry 4.0 — Current Challenges in Information Systems. Studies in Computational Intelligence, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-030-40417-8_3
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