Abstract
Strategic planning is a practical approach for researchers to conduct the STEEP analysis. One of the most promising approaches for strategic planning is the Foresight Framework. In the very first steps of Foresight Framework, however, the environmental scanning is involved. This process is time-consumed since a very large amount of data must be explored. To alleviate the time in such a process, this study proposes a design and an implementation of the forecast sentence extractor by using natural language processing and machine learning algorithm. The proposed algorithm digests a long article and then provides a short list of forecast sentences. Three feature selection approaches are tested. From the experimental studies, the accuracy of the proposed algorithm is up to 85.10%.
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Acknowledgements
The authors aspire to thank Mr. Tossapol Ramingwong and his team from Thailand Productivity Institute (FTPI), Thailand, for providing Foresight Framework concept and requirement as well as Dr. Sumavasee Salasuk and her team from Digital Economy Promotion Agency (DEPA), Thailand, for delineating Foresight Framework requirement in DEPA point of view. Special thanks go to Mr. Suchart Imbunchon, Mr. Kampon Hannaruechai, Mr. Mahaysak Kanignant, Mr. Khantipong Damkham, and development team at AI Lab, Betimes Solution Co., Ltd. for helping in requirement analysis, data acquisition, and cleansing processes, as well as project funding. Last, but not least, the authors want to thank all faculty members in the Department of Computer Engineering and Artificial Intelligence, UTCC, for providing challenging comments and creative discussion.
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Srichareon, B., Manitpornsut, S., Pongdamrong, P. (2020). A Design and an Implementation of Forecast Sentence Extractor. In: Jain, L., Tsihrintzis, G., Balas, V., Sharma, D. (eds) Data Communication and Networks. Advances in Intelligent Systems and Computing, vol 1049. Springer, Singapore. https://doi.org/10.1007/978-981-15-0132-6_18
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DOI: https://doi.org/10.1007/978-981-15-0132-6_18
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