A text analytics model for agricultural knowledge discovery and sustainable food production: A case study from Oklahoma Panhandle

Decision Analytics Journal - Tập 9 - Trang 100350 - 2023
Ali Bagheri1, Saleh Taghvaeian2, Dursun Delen3,4
1Department of Management Science & Information Systems, Oklahoma State University, Stillwater, OK, USA
2Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE USA
3Department of Industirial Engineering, Faculty of Engineering and Natural Sciences, Istinye University, 34396, Istanbul, Turkey
4Center for Health Systems Innovation, Department of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Stillwater, 74078, OK, USA

Tài liệu tham khảo

Goodchild, 2007, Citizens as sensors: The world of volunteered geography, GeoJournal, 69, 10.1007/s10708-007-9111-y Liu, 2015, Social sensing: A new approach to understanding our socioeconomic environments, Ann. Assoc. Am. Geogr., 105, 10.1080/00045608.2015.1018773 Zhu, 2020, Social weather: A review of crowdsourcing-assisted meteorological knowledge services through social cyberspace, Geosci. Data J., 7, 10.1002/gdj3.85 O’Mara-Eves, 2015, Using text mining for study identification in systematic reviews: A systematic review of current approaches, Syst. Rev., 4 Poncio, 2023, An investigation of the gender gap in the information technology and engineering programs through text mining, Decis. Anal. J., 6 Nishant, 2016, Sustainability and differentiation: Understanding materiality from the context of Indian firms, J. Bus. Res., 69, 1892, 10.1016/j.jbusres.2015.10.075 Schutte, 2022, Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature, J. Biomed. Inform., 131, 10.1016/j.jbi.2022.104120 Guo, 2021, Process-extraction-based text similarity measure for emergency response plans, Expert Syst. Appl. Timoshenko, 2019, Identifying customer needs from user-generated content, Mark. Sci., 38, 1, 10.1287/mksc.2018.1123 Garner, 2022, Utilizing text-mining to explore consumer happiness within tourism destinations, J. Bus. Res., 139, 1366, 10.1016/j.jbusres.2021.08.025 Roeder, 2022, Data-driven decision-making in credit risk management: The information value of analyst reports, Decis. Support Syst., 158, 10.1016/j.dss.2022.113770 Zolbanin, 2021, Miscommunication in the age of communication: A crowdsourcing framework for symptom surveillance at the time of pandemics, Int. J. Med. Inf., 151, 10.1016/j.ijmedinf.2021.104486 Biswas, 2022, A text-mining based cyber-risk assessment and mitigation framework for critical analysis of online hacker forums, Decis. Support Syst., 152, 10.1016/j.dss.2021.113651 Drury, 2019, A survey of the applications of text mining for agriculture, Comput. Electron. Agric., 163, 10.1016/j.compag.2019.104864 Valsamidis, 2013, A framework for opinion mining in blogs for agriculture, Proc. Technol., 8 Jones-Garcia, 2022 Sodoge, 2023, Automatized spatio-temporal detection of drought impacts from newspaper articles using natural language processing and machine learning, Weather Clim. Extrem., 41 Mahon, 2023, The application of a sentiment analysis approach to explore public understandings of animal agriculture, J. Rural Stud., 103, 10.1016/j.jrurstud.2023.103127 Meena, 2023, A hybrid deep learning approach for detecting sentiment polarities and knowledge graph representation on monkeypox tweets, Decis. Anal. J., 7 Sufi, 2022, A decision support system for extracting artificial intelligence-driven insights from live twitter feeds on natural disasters, Decis. Anal. J., 5 Hardt, 2024, A social media analysis of travel preferences and attitudes, before and during Covid-19, Tour. Manag. Zou, 2023, PreBit — A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin, Expert Syst. Appl., 233, 10.1016/j.eswa.2023.120838 Mehra, 2023, A social media analytics application of impression management and social presence theories to Twitter interaction analysis, Decis. Anal. J., 10.1016/j.dajour.2023.100321 Zipper, 2018, Agricultural research using social media data, Agron. J., 110, 10.2134/agronj2017.08.0495 P. Shankar, C. Bitter, M. Liwicki, Digital Crop Health Monitoring by Analyzing Social Media Streams, in: 2020 IEEE/ ITU International Conference on Artificial Intelligence for Good, AI4G 2020, 2020, http://dx.doi.org/10.1109/AI4G50087.2020.9310985. Smith, 2020, Calibrating human attention as indicator monitoring #drought in the twittersphere, Bull. Am. Meteorol. Soc., 101, 10.1175/BAMS-D-19-0342.1 Pan, 2020, The influence of COVID-19 on agricultural economy and emergency mitigation measures in China: A text mining analysis, PLoS One, 15, 10.1371/journal.pone.0241167 Riley, 2021, #farming365 – Exploring farmers’ social media use and the (re)presentation of farming lives, J. Rural Stud., 87, 99, 10.1016/j.jrurstud.2021.08.028 Shangguan, 2021 B. Zhang, F. Schilder, K.H. Smith, M.J. Hayes, S. Harms, T. Tadesse, TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data, in: Tackling Climate Change with Machine Learning Workshop at ICML, 2021. Mukherjee, 2022, Feasibility of adding Twitter data to aid drought depiction: Case study in Colorado, Water, 14, 10.3390/w14182773 Singh, 2022, Sentiment analysis of Twitter data during farmers’ protest in India through machine learning, vol. 12, 1 Rotz, 2018, Drawing lines in the cornfield: an analysis of discourse and identity relations across agri-food networks, Agric. Hum. Values, 35, 10.1007/s10460-017-9838-0 Masasi, 2019, Simulating soil water content, evapotranspiration, and yield of variably irrigated grain sorghum using AquaCrop, J. Am. Water Resour. Assoc., 55, 10.1111/1752-1688.12757 Taghvaeian, 2015, Evaluating the impact of drought on surface and groundwater dependent irrigated agriculture in western oklahoma Handa, 2019, The efficiencies, environmental impacts and economics of energy consumption for groundwater-based irrigation in Oklahoma, Agriculture, 9, 10.3390/agriculture9020027 Hassani, 2021, A geographical survey of center pivot irrigation systems in the central and southern high plains aquifer region of the United States, Appl. Eng. Agric., 37, 10.13031/aea.14693 Akyuz, 2017 Liu, 2010, Sentiment analysis and subjectivity, 2 O’Leary, 2011, Blog mining-review and extensions: From each according to his opinion, Decis. Support Syst., 51 V. Labatut, H. Cherifi, Accuracy measures for the comparison of classifiers, in: The 5th International Conference on Information Technology, 2011. Witten, 2016, Data mining: Practical machine learning tools and techniques Sharma S., 2021, Personal communications. Edwards, 2011, Impact of dual-purpose management on wheat grain yield, Crop Sci., 51, 10.2135/cropsci2011.01.0043 de Oliveira Silva, 2020 McPherson, 2007, Statewide monitoring of the mesoscale environment: A technical update on the oklahoma mesonet, J. Atmos. Ocean. Technol., 24, 10.1175/JTECH1976.1 Liu, 2012, Sentiment analysis and opinion mining, Synthesis Lectures on Human Language Technologies, 5, 10.1007/978-3-031-02145-9 S. Brody, N. Diakopoulos, Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! using word lengthening to detect sentiment in microblogs, in: EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 2011. Fersini, 2016, Expressive signals in social media languages to improve polarity detection, Inf. Process. Manage., 52, 20, 10.1016/j.ipm.2015.04.004 Peng, 2019, Discovering the influence of sarcasm in social media responses, Wiley Interdiscip. Rev.: Data Min. Knowl. Discov., 9 Sarsam, 2020, Sarcasm detection using machine learning algorithms in Twitter: A systematic review, Int. J. Market Res., 62, 578, 10.1177/1470785320921779