References
- Aggarwal, C. C., and C. Zhai. 2012. An introduction to text mining. In Mining text data, ed. Charu C. Aggarwal, ChengXiang Zhai, 1–10. Boston, MA: Springer. doi:10.1007/978-1-4614-3223-4.
- Aldowah, H., S. U. Rehman, and I. Umar. 2018. Security in Internet of Things: Issues, challenges and solutions. Proceedings of the 3rd International Conference of Reliable Information and Communication Technology 2018 (IRICT 2018), Kuala Lumpur, Malaysia, July 23–24..
- An overview of text mining visualisations possibilities with R on the CETA trade agreement. n.d. Accessed July 3, 2019. https://www.bnosac.be/index.php/blog/56-an-overview-of-text-mining-visualisations-possibilities-with-r-on-the-ceta-trade-agreement.
- Atzori, L., A. Iera, and G. Morabito. 2010. The Internet of Things: A survey. Computer Networks 54 (15):2787–805. doi:10.1016/j.comnet.2010.05.010.
- Bach, P., Ž. K. Mirjana, S. Seljan, and L. Turulja. 2019. Text mining for big data analysis in financial sector: A literature review. Sustainability 11 (5):1277. doi:10.3390/su11051277.
- Balaji, M. S., S. K. Roy, A. Sengupta, and A. Chong. 2018. User acceptance of IoT applications in retail industry. In The Internet of Things in the Modern Business Environment,ed. In Lee, 1331–52. USA: IGI Global. doi:10.4018/978-1-5225-2104-4.ch002.
- Bastani, K., H. Namavari, and J. Shaffer. 2019. Latent Dirichlet Allocation (LDA) for topic modeling of the CFPB Consumer Complaints. Expert Systems with Applications 127 (August):256–71. doi:10.1016/j.eswa.2019.03.001.
- Bauer, H., O. Burkacky, and C. Knochenhauer. 2017. Security in the Internet of Things. 2017. https://www.mckinsey.com/industries/semiconductors/our-insights/security-in-the-internet-of-things.
- Benoit, K., and A. Obeng. 2019. Readtext: Import and handling for plain and formatted text files. R package version 0.75. https://CRAN.R-project.org/package=readtext.
- Blei, D. M., A. Y. Ng, and M. I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3 (Jan):993–1022.
- Breivold, H. P., and S. Kristian. 2015. Internet of Things for industrial automation–challenges and technical solutions. In 2015 IEEE International Conference on Data Science and Data Intensive Systems, 532–39. IEEE, Sydney, NSW, Australia, December 11–13. doi:10.1109/DSDIS.2015.11
- Buntz, B. 2016. Top 10 reasons people Aren’t Embracing the IoT. 2016. https://www.iotworldtoday.com/2016/04/20/top-10-reasons-people-aren-t-embracing-iot/.
- Canhoto, A. I., and S. Arp. 2017. Exploring the factors that support adoption and sustained use of health and fitness wearables. Journal of Marketing Management 33 (1–2):32–60. doi:10.1080/0267257X.2016.1234505.
- Chang, C., S. N. Srirama, and R. Buyya. 2019. Internet of Things (IoT) and new computing paradigms. In Fog and Edge Computing: Principles and Paradigms ed. Rajkumar Buyya, Satish Narayana Srirama, 1–23, Hoboken, NJ: John Wiley & Sons, Inc. doi:10.1109/DSDIS.2015.11
- Chang, J., S. Gerrish, C. Wang, B.-G. Jordan L, and D. M. Blei. 2009. Reading tea leaves: How humans interpret topic models. Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 7–10.
- Coughlan, T., M. Brown, R. Mortier, R. J. Houghton, M. Goulden, and G. Lawson. 2012. Exploring acceptance and consequences of the Internet of Things in the home. In Green Computing and Communications (GreenCom), 2012 IEEE International Conference On, 148–55. Besancon, France: IEEE. doi:10.1109/GreenCom.2012.32
- Csardi, G. n.d. R Igraph manual pages. Accessed July 3, 2019. https://igraph.org/r/doc/.
- Csardi, G., and T. Nepusz. 2006. The Igraph software package for complex network research. InterJournal Complex Systems 1695.
- D’mello, A. 2019. The Top 3 challenges of preparing IoT data. 2019. https://www.iot-now.com/2019/02/08/92826-top-3-challenges-preparing-iot-data/.
- Delaney, K., and E. Levy. 2017. Internet of Things: Challenges, breakthroughs and best practices. 2017. https://connectedfutures.cisco.com/report/internet-of-things-challenges-breakthroughs-and-best-practices/.
- Dowle, M., and A. Srinivasan. 2019. Data table: Extension of `data.Frame`. R package version 1.12.2. https://CRAN.R-project.org/package=data.table
- Evans, C. 2018. Internet of Things challenges in storage and data. 2018. https://www.computerweekly.com/news/252450705/Internet-of-things-challenges-in-storage-and-data.
- Feinerer, I. 2018. Introduction to thetmpackagetext mining in R. https://cran.r-project.org/web/packages/tm/vignettes/tm.pdf.
- Feinerer, I., and K. Hornik. 2018. Tm: Text mining package. R package version 0.7-6. https://CRAN.R-project.org/package=tm.
- Feinerer, I., K. Hornik, and D. Meyer. 2008. Text mining infrastructure in R. Journal of Statistical Software 25 (5):1–54. doi:10.18637/jss.v025.i05.
- Fellows, I. 2018a. Package ‘Wordcloud’. https://cran.r-project.org/web/packages/wordcloud/wordcloud.pdf.
- Fellows, I. 2018b. Wordcloud: Word clouds. R package version 2.6. https://CRAN.R-project.org/package=wordcloud.
- Fetzer, T. 2017. Topic models. http://www.trfetzer.com/wp-content/uploads/EC994_Topic-Modeling1.pdf.
- Garcia-Rudolph, A., S. Laxe, J. Saurí, and M. B. Guitart. 2019. Stroke survivors on twitter: Sentiment and topic analysis from a gender perspective. Journal of Medical Internet Research 21 (8):e14077. doi:10.2196/14077.
- Garg, R., and J. Kim. 2018. An exploratory study for understanding reasons of (not-) using Internet of Things. CHI '18: CHI Conference on Human Factors in Computing Systems Montreal QC Canada April, 2018. New York, NY: Association for Computing Machinery. ISBN:978-1-4503-5621-3
- Griffiths, T. L., and M. Steyvers. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences 101 (suppl 1):5228–35. doi:10.1073/pnas.0307752101.
- Grün, B., and K. Hornik. 2011. Topicmodels: An R package for fitting topic models. Journal of Statistical Software 40 (13):1–30. doi:10.18637/jss.v040.i13.
- Gubbi, J., R. Buyya, S. Marusic, and M. Palaniswami. 2013. Internet of Things (IoT): A Vision, architectural elements, and future directions. Future Generation Computer Systems 29 (7):1645–60. doi:10.1016/j.future.2013.01.010.
- Hanson, J. 2014. 5 Challenges of Internet of Things Connectivity. 2014. https://community.arm.com/iot/b/blog/posts/5-challenges-of-internet-of-things-connectivity.
- Hornik, K., D. Meyer, and C. Buchta. 2019. Slam: Sparse lightweight arrays and matrices. R package version 0.1-45. https://CRAN.R-project.org/package=slam.
- Huang, J., Q. Duan, C.-C. Xing, and H. Wang. 2017. Topology control for building scalable energy-efficient Internet of Things. IEEE Wireless Communications 24:1. doi:10.1109/MWC.2017.1600193WC.
- Jockers, M. L. 2015. Syuzhet: Extract sentiment and plot arcs from text. https://github.com/mjockers/syuzhet.
- Katz, H. 2019. IoT cybersecurity challenges and solutions. 2019. https://www.allot.com/blog/iot_cybersecurity_challenges_and_solutions/.
- Lee, I., and K. Lee. 2015. The Internet of Things (IoT): Applications, Investments, and Challenges for Enterprises. Business Horizons 58 (4):431–40. doi:10.1016/j.bushor.2015.03.008.
- Liu, B. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies. 5. Morgan & Claypool. doi:10.2200/S00416ED1V01Y201204HLT016
- Ma, M., P. Wang, and C.-H. Chu. 2013. Data management for Internet of Things: Challenges, approaches and opportunities. In 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, 1144–51. Beijing, China: IEEE.
- MacDonald, K. I., and V. Dressler. 2018. Using citation analysis to identify research fronts: A case study with the Internet of Things. Science & Technology Libraries 37 (2):171–86. doi:10.1080/0194262X.2017.1415183.
- Mair, P. 2018. Modern psychometrics with R. Springer International Publishing. Use R!.
- Manyika, J., M. Chui, P. Bisson, J. Woetzel, R. Dobbs, J. Bughin, and D. Aharon. 2015. Unlocking the potential of the Internet of Things. McKinsey Global Institute. https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world.
- Mishra, D., A. Gunasekaran, S. J. Childe, T. Papadopoulos, R. Dubey, and S. Wamba. 2016. Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature. Industrial Management & Data Systems 116 (7):1331–55. doi:10.1108/IMDS-11-2015-0478.
- Mohammad, S. 2011a. From once upon a time to happily ever after: Tracking emotions in novels and fairy tales. Proceedings of the ACL Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH), 2011, Portland, OR.
- Mohammad, S. M. 2011b. Even the abstract have colour: Consensus in Word-Colour associations. n Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-Volume 2, 368–73. Portland, Oregon: Association for Computational Linguistics.
- Mohammad, S. M., and P. D. Turney. 2010. Emotions evoked by common words and phrases: Using mechanical Turk to create an emotion Lexicon. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 26–34. Association for Computational Linguistics. https://dl.acm.org/doi/10.5555/1860631.1860635
- Mohammad, S. M., and P. D. Turney. 2012. Crowdsourcing a word–emotion association Lexicon. Computational Intelligence 29 (3):436–65. doi:10.1111/j.1467-8640.2012.00460.x.
- Mohammad, S. M., and T. W. Yang. 2011. Tracking sentiment in Mail: How genders differ on emotional axes. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, 70–79. Portland, OR: Association for Computational Linguistics.
- Mohammadzadeh, A. K., S. Ghafoori, A. Mohammadian, R. Mohammadkazemi, B. Mahbanooei, and R. Ghasemi. 2018. A Fuzzy analytic network process (FANP) approach for prioritizing Internet of Things challenges in Iran. Technology in Society 53:124–34. doi:10.1016/j.techsoc.2018.01.007.
- Momtazi, S., and F. Naumann. 2013. Topic modeling for expert finding using latent dirichlet allocation. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3 (5):346–53. doi:10.1002/widm.1102.
- Moro, S., P. Cortez, and P. Rita. 2015. Business Intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent dirichlet allocation. Expert Systems with Applications 42 (3):1314–24. doi:10.1016/j.eswa.2014.09.024.
- Mumtaz, S., A. Alsohaily, Z. Pang, A. Rayes, K. F. Tsang, and J. Rodriguez. 2017. Massive Internet of Things for industrial applications: Addressing wireless IIoT Connectivity challenges and ecosystem fragmentation. IEEE Industrial Electronics Magazine 11 (1):28–33. doi:10.1109/MIE.2016.2618724.
- Neuwirth, E. n.d. Package ‘RColorBrewer. https://cran.r-project.org/web/packages/RColorBrewer/RColorBrewer.pdf.
- Nikita, M. 2017. Ldatuning: Tuning of the Latent Dirichlet allocation models parameters. R package version 0.2.2. https://github.com/nikita-moor/ldatuning.
- Nwazor, T. 2018. IoT security challenges and 5 effective ways to handle them. 2018. https://dzone.com/articles/iot-security-challenges-and-5-effective-ways-to-ha-1.
- Ognyanova, K. 2015. Network visualization with R. POLNET 2015 Workshop, Portland OR. http://www.kateto.net/wp-content/uploads/2015/06/Polnet%202015%20Network%20Viz%20Tutorial%20-%20Ognyanova.pdf.
- Oliver, J. 2018. Heatmaps in R. 2018. https://jcoliver.github.io/learn-r/006-heatmaps.html.
- Peter, R. J., and B. Richard Watson. 2017. Research challenges for the Internet of Things: What role can or play? Systems 5:1. doi:10.3390/systems5010024.
- Popescul, D., and M. Georgescu. 2014. Internet of Things–some ethical issues. The USV Annals of Economics and Public Administration 13 (2 (18)):208–14.
- R Core Team. 2018. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
- Rajman, M., and M. Vesely. 2004. From text to knowledge: document processing and visualization: A text mining approach. In Text mining and its applications, ed. Spiros Sirmakessis, 7–24. Berlin, Heidelberg: Springer. doi:10.1007/978-3-540-45219-5
- Randy, Q. 2018. Systematic literature review with quantitative analysis of scientific production: Scientometrics using R. 2018. http://rstudio-pubs-static.s3.amazonaws.com/410444_09a66192d93a467cb8e67c427b8b5277.html.
- Read Text Files with Readtext. n.d. Accessed July 3, 2019. https://cran.r-project.org/web/packages/readtext/vignettes/readtext_vignette.html.
- Rose, K., S. Eldridge, and L. Chapin. 2015. The Internet of Things: An overview–understanding the issues and challenges of a more connected world. United States: The Internet Society (ISOC).
- Roussey, B. 2016. 5 challenges facing the Internet of Things. 2016. http://techgenix.com/internet-of-things-challenges/.
- Rowe, M. n.d. DevOps challenges in the Internet of Things. Accessed July 21, 2019. https://techbeacon.com/enterprise-it/devops-challenges-internet-things.
- Samuel, S. S. I. 2016. A review of connectivity challenges in IoT-smart home. In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), 1–4. doi:10.1109/ICBDSC.2016.7460395.
- Sfar, R., E. N. Arbia, Y. Challal, and Z. Chtourou. 2018. A roadmap for security challenges in the Internet of Things. Digital Communications and Networks 4 (2):118–37. doi:10.1016/j.dcan.2017.04.003.
- Shen, W. 2017. Text and sentiment analysis on the world’s most read book. 2017. https://rpubs.com/wes-shen/sentiment-analysis-most-read-book.
- Sicari, S., A. Rizzardi, L. A. Grieco, and A. Coen-Porisini. 2015. Security, privacy and trust in Internet of Things: The road ahead. Computer Networks 76 (January):146–64. doi:10.1016/j.comnet.2014.11.008.
- Sievert, C., and K. Shirley. 2014. LDAvis: A method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, 63–70. Baltimore, MD.
- Sievert, C., and K. Shirley. 2015. LDAvis: Interactive visualization of topic models. R package version 0.3.2. https://CRAN.R-project.org/package=LDAvis.
- Silge, J., and D. Robinson. 2016. Tidytext: Text mining and analysis using tidy data principles in R. Journal of Open Source Software 1 (3):37. doi:10.21105/joss.00037.
- Silge, J., and D. Robinson. 2017. Text mining with R: A tidy approach. USA: O’Reilly Media.
- Srivastava, T., P. Desikan, and V. Kumar. 2005. Web mining–concepts, applications and research directions. In Foundations and advances in data mining, ed. Wesley Chu, Tsau Young Lin, 275–307. Berlin, Heidelberg: Springer.
- Text Mining Example Codes (Tweets). n.d. Accessed July 3, 2019. https://rstudio-pubs-static.s3.amazonaws.com/66739_c4422a1761bd4ee0b0bb8821d7780e12.html.
- Tian, K., M. Revelle, and D. Poshyvanyk. 2009. Using latent dirichlet allocation for automatic categorization of software. In 2009 6th IEEE International Working Conference on Mining Software Repositories, 163–66. doi:10.1109/MSR.2009.5069496.
- Tirunillai, S., and G. J. Tellis. 2014. Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent dirichlet allocation. Journal of Marketing Research 51 (4):463–79. doi:10.1509/jmr.12.0106.
- Trappey, A. J. C., C. V. Trappey, U. H. Govindarajan, A. C. Chuang, and J. J. Sun. 2017. A review of essential standards and patent landscapes for the Internet of Things: A key enabler for industry 4.0. Advanced Engineering Informatics 33 (August):208–29. doi:10.1016/j.aei.2016.11.007.
- Tsai, C.-W., C.-F. Lai, and A. V. Vasilakos. 2014. Future Internet of Things: open issues and challenges. Wireless Networks 20 (8):2201–17. doi:10.1007/s11276-014-0731-0.
- Wang, S.-H., Y. Ding, W. Zhao, Y.-H. Huang, R. Perkins, W. Zou, and J. J. Chen. 2016. Text mining for identifying topics in the literatures about adolescent substance use and depression. BMC Public Health 16 (1):279. doi:10.1186/s12889-016-2932-1.
- Whitmore, A., A. Agarwal, and D. X. Li. 2015. The Internet of Things—A survey of topics and trends. Information Systems Frontiers 17 (2):261–74. doi:10.1007/s10796-014-9489-2.
- Wickham, H. 2016. Ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.
- Wickham, H. 2017. Tidyverse: Easily install and load the “Tidyverse.” R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse.
- Wickham, H., and L. Henry 2019. Tidyr: Easily tidy data with “spread()” and “Gather()” functions. R package version 0.8.3. https://CRAN.R-project.org/package=tidyr.
- Wickham, H., R. François, L. Henry, and M. Kirill 2019. Dplyr: A grammar of data manipulation. R package version 0.8.0.1. https://CRAN.R-project.org/package=dplyr.
- Xiao, N., and L. Miaozhu 2019. Scientific journal and sci-fi themed color palettes for ggplot2. 2019. https://nanx.me/ggsci/articles/ggsci.html.
- Zhao, Y. 2012. R and data mining: Examples and case studies. USA: Academic Press.
- Zhao, Y. n.d. Social network analysis Accessed July 3, 2019. http://www.rdatamining.com/examples/social-network-analysis.
- Ziegeldorf, J. H., O. G. Morchon, and K. Wehrle. 2014. Privacy in the Internet of Things: Threats and challenges. Security and Communication Networks 7 (12):2728–42. doi:10.1002/sec.795.
- Zubiaga, A., R. Procter, and C. Maple. 2018. A longitudinal analysis of the public perception of the opportunities and challenges of the internet of things. PLoS ONE 13 (12):e0209472. doi:10.1371/journal.pone.0209472.