Assessment of text-generated supply chain risks considering news and social media during disruptive events

[1]  W. Nejdl,et al.  Towards sentiment and Temporal Aided Stance Detection of climate change tweets , 2023, Inf. Process. Manag..

[2]  G. Alaghband,et al.  Enhancing Financial Market Analysis and Prediction with Emotion Corpora and News Co-Occurrence Network , 2023, Journal of Risk and Financial Management.

[3]  R. Lahoz-Beltra,et al.  Press media impact of the Cumbre Vieja volcano activity in the island of La palma (Canary Islands): A machine learning and sentiment analysis of the news published during the volcanic eruption of 2021 , 2023, International Journal of Disaster Risk Reduction.

[4]  M. Koopmans,et al.  Tracking SARS-CoV-2 variants and resources , 2023, Nature Methods.

[5]  R. Rajan,et al.  What Purpose Do Corporations Purport? Evidence from Letters to Shareholders , 2023, SSRN Electronic Journal.

[6]  Cheng Qian,et al.  Understanding public opinions on social media for financial sentiment analysis using AI-based techniques , 2022, Inf. Process. Manag..

[7]  Liangcheng Xu,et al.  Using social media information to predict the credit risk of listed enterprises in the supply chain , 2022, Kybernetes.

[8]  M. R. Sheikhattar,et al.  A thematic analysis–based model for identifying the impacts of natural crises on a supply chain for service integrity: a text analysis approach , 2022, Environmental Science and Pollution Research.

[9]  D. Barceló,et al.  Russian-Ukrainian war impacts the total environment. , 2022, The Science of the total environment.

[10]  P. Kalpana,et al.  Supply chain risk identification: a real-time data-mining approach , 2022, Ind. Manag. Data Syst..

[11]  Ukraine: Note on the impact of the war on food security in Ukraine , 2022 .

[12]  Takuma Matsuda,et al.  Uncovering the impact of COVID-19 on shipping and logistics , 2021, Maritime Business Review.

[13]  S. Seuring,et al.  The Impact of the Coronavirus Pandemic on Supply Chains and Their Sustainability: A Text Mining Approach , 2021, Frontiers in Sustainability.

[14]  M. Freitag,et al.  Text Mining for Supply Chain Risk Management in the Apparel Industry , 2021, Applied Sciences.

[15]  P. Brandtner,et al.  Impact of COVID-19 on the Customer End of Retail Supply Chains: A Big Data Analysis of Consumer Satisfaction , 2021 .

[16]  Vahid Sohrabpour,et al.  Artificial intelligence in supply chain management: A systematic literature review , 2021, Journal of Business Research.

[17]  Julie Stal-Le Cardinal,et al.  The main trends for multi-tier supply chain in Industry 4.0 based on Natural Language Processing , 2020, Comput. Ind..

[18]  Xiande Zhao,et al.  Application of text mining in identifying the factors of supply chain financing risk management , 2020, Ind. Manag. Data Syst..

[19]  Daniel R. Eyers,et al.  Using simulation to explore the influence of online reviews on supply chain dynamics , 2020, Comput. Ind. Eng..

[20]  Mabel C. Chou,et al.  Lessons Learned from the COVID-19 Pandemic Exposing the Shortcomings of Current Supply Chain Operations: A Long-Term Prescriptive Offering , 2020, Sustainability.

[21]  Amalesh Sharma,et al.  Covid-19′s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data , 2020, Journal of Business Research.

[22]  George P. Ball,et al.  Does social media elevate supply chain importance? An empirical examination of supply chain glitches, Twitter reactions, and stock market returns , 2020 .

[23]  Duncan McFarlane,et al.  Extracting supply chain maps from news articles using deep neural networks , 2020, Int. J. Prod. Res..

[24]  R. Shanmugam Practical text analytics: maximizing the value of text data , 2019, Journal of Statistical Computation and Simulation.

[25]  Ray Qing Cao,et al.  Using sentiment analysis to improve supply chain intelligence , 2017, Information Systems Frontiers.

[26]  Tong Che,et al.  Information sharing and the impact of shutdown policy in a supply chain with market disruption risk in the social media era , 2019, Inf. Manag..

[27]  Abhinav Kumar,et al.  Location reference identification from tweets during emergencies: A deep learning approach , 2019, International Journal of Disaster Risk Reduction.

[28]  Murugan Anandarajan,et al.  Practical Text Analytics , 2018, Advances in Analytics and Data Science.

[29]  Antonio Moreno,et al.  The Operational Value of Social Media Information , 2018 .

[30]  Xuening Chu,et al.  Identification of the to-be-improved product features based on online reviews for product redesign , 2018, Int. J. Prod. Res..

[31]  Ashlea Bennett Milburn,et al.  A general framework for assessing the value of social data for disaster response logistics planning , 2018, Eur. J. Oper. Res..

[32]  Sundaravalli Narayanaswami Digital social media: Enabling performance quality of Indian Railway services , 2018, Journal of Public Affairs.

[33]  T. Nisar,et al.  Trains and Twitter: Firm generated content, consumer relationship management and message framing , 2018, Transportation Research Part A: Policy and Practice.

[34]  Chuan-Jun Su,et al.  Risk assessment for global supplier selection using text mining , 2018, Comput. Electr. Eng..

[35]  Jing Gao,et al.  A deep learning approach for detecting traffic accidents from social media data , 2018, ArXiv.

[36]  V. Parida,et al.  Social media engagement strategy: Investigation of marketing and R&D interfaces in manufacturing industry , 2017, Industrial Marketing Management.

[37]  Deborah E. White,et al.  Thematic Analysis , 2017 .

[38]  Lincoln C. Wood,et al.  Think exogenous to excel: alternative supply chain data to improve transparency and decisions , 2017 .

[39]  Ted Kwartler,et al.  Text Mining in Practice with R , 2017 .

[40]  Yogesh Kumar Dwivedi,et al.  Interpretive structural modelling and fuzzy MICMAC approaches for customer centric beef supply chain: application of a big data technique , 2017 .

[41]  Nishikant Mishra,et al.  Social media data analytics to improve supply chain management in food industries , 2017, Transportation Research Part E: Logistics and Transportation Review.

[42]  Liang Guo,et al.  Automated competitor analysis using big data analytics: Evidence from the fitness mobile app business , 2017, Bus. Process. Manag. J..

[43]  M. Gentzkow,et al.  Text As Data , 2017, Journal of Economic Literature.

[44]  Paloma Díaz,et al.  Giving meaning to tweets in emergency situations: a semantic approach for filtering and visualizing social data , 2016, SpringerPlus.

[45]  Tammo H. A. Bijmolt,et al.  To Keep or Not to Keep: Effects of Online Customer Reviews on Product Returns , 2016 .

[46]  Lincoln C. Wood,et al.  Exploring Sentiment Analysis to Improve Supply Chain Decisions , 2015 .

[47]  B. Chae,et al.  Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research , 2015 .

[48]  N. Davies,et al.  Developing a smartphone app to enhance Oxfam's supply chain visibility , 2015 .

[49]  Patrice Bellot,et al.  Accurate and effective latent concept modeling for ad hoc information retrieval , 2014, Document Numérique.

[50]  S. Chopra,et al.  Reducing the Risk of Supply Chain Disruptions , 2014 .

[51]  Saif Mohammad,et al.  CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON , 2013, Comput. Intell..

[52]  Justin Grimmer,et al.  Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts , 2013, Political Analysis.

[53]  S. Davis,et al.  Measuring Economic Policy Uncertainty , 2013 .

[54]  Kurt Hornik,et al.  topicmodels : An R Package for Fitting Topic Models , 2016 .

[55]  Michael Ehrmann,et al.  Central Bank Communication on Financial Stability , 2011, SSRN Electronic Journal.

[56]  Tommaso Rossi,et al.  Managing operational risks along the oil supply chain , 2010 .

[57]  Sheng Tang,et al.  A density-based method for adaptive LDA model selection , 2009, Neurocomputing.

[58]  Robert B. Handfield,et al.  Introduction to Operations and Supply Chain Management , 2005 .

[59]  F. Caniato,et al.  BUILDING A SECURE AND RESILIENT SUPPLY NETWORK. , 2003 .

[60]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[61]  John J. Sviokla,et al.  Exploiting the virtual value chain , 1999 .

[62]  O. Tokarchuk,et al.  How much is too much? Estimating tourism carrying capacity in urban context using sentiment analysis , 2022, Tourism Management.

[63]  Eric D. Smith,et al.  Text Mining to Understand the Influence of Social Media Applications on Smartphone Supply Chain , 2018 .

[64]  KhanM. Laeeq Social media engagement , 2017 .

[65]  Jeffrey M. Wooldridge,et al.  Introductory Econometrics: A Modern Approach , 1999 .

[66]  Paul C. Tetlock Giving Content to Investor Sentiment: The Role of Media in the Stock Market , 2005, The Journal of Finance.