Significance of Natural Language Processing in Data Analysis Using Business Intelligence:

Jayashree Rajesh School of Computing Sciences, VISTAS, India Priya Chitti Babu School of Computing Sciences, VISTAS, India Significance of Natural Language Processing in Data Analysis Using Business Intelligence

In the current machine-centric world, humans expect a lot from machines right from waking us up. We expect them to do activities like reminding us on traffic, tracking of appointments, etc. The smart devices we have with us are creating a constructive impact on our day-to-day lives. Many of us have not thought about the communication between ourselves and the devices we have and the language we use for communication. Natural language processing runs behind all these activities and is currently playing a vital role with respect to the communication with humans with the use of virtual assistants like Alexa, Siri, and search engines like Bing, Google, etc. This implies that we are talking with the machines as if they are human. The advanced natural language processing techniques have drastically modified the way to discover and interact with data. In the recent world, the same advanced techniques are primarily used in the data analysis using NLP in business intelligence tools. This chapter elaborates the significance of natural language processing in business intelligence.
chapter 9 2021 169 188 10.4018/978-1-7998-7728-8.ch009 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-7728-8.ch009 https://www.igi-global.com/viewtitle.aspx?TitleId=284208 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology S.Asur 492 2010) 10.1109/WI-IAT.2010.63 Predicting the future with social media. 10.1109/ICCS45141.2019.9065380 Bafna, A., Parkh, A., Iyer, A., & Halbe, A. (2019). A Novel approach to Data Visualization by supporting Ad-hoc Query and Predictive analysis. Proceedings of the International Conference on Intelligent Computing and Control Systems ICICCS 2019. Journal of Computational Science J.Bollen 2011 Twitter mood predicts the stock market 10.1109/ICAETR.2014.7012883 Gupta, D., & Siddiqui, S. (2014). Big Data Implementation and Visualization. IEEE International Conference on Advances in Engineering & Technology Research, ICAETR - 2014. 10.35940/IJEAT Proceedings of ICTIDS 2019. G.Jayashree 2019) Comprehensive Guide to Implementation of Datawarehouse in Education Proceedings of ICCSEA 2020 G.Jayashree 2020) Data Integration with XML ETL Processing 10.1109/ICECA.2018.8474639 Kate, A., Kamble, S., Bodke, A., & Joshi, M. (2018). Conversion of Natural Language Query to SQL Query. IEEE Second International Conference on Electronics, Communication and Aerospace Technology, ICECA - 2018. Second International Conference on Computational Intelligence & Communication Technology O.Kumar 2016) Visualization: A novel approach for big data analytics 10.1109/IC3I.2016.7918044 Mohd Ali, S., Gupta, N., Nayak, G. K., & Lenka, R. (2016). Big data visualization: Tools and challenges. IEEE-2nd International Conference on Contemporary Computing and Informatics, IC3I 2016. 10.1109/ISS1.2017.8389287 Reshma, E. U., & Remya, P. C. (2017). A Review of Different Approaches in Natural Language Interfaces to Databases. Proceedings of the International Conference on Intelligent Sustainable Systems ICISS 2017. 2019 IEEE International Conference on Big Data A.Saini 2019) 10.1109/BigData47090.2019.9005961 A Novel Code-Switching Behaviour Analysis in Social Media Discussions Natural Language Processing Selforganizology G.Singh 3 3 100 2016 An algorithm to transform natural language into SQL queries for relational databases. International Academy of Ecology and Environmental Sciences (IAEES) 10.1109/ICOEI.2019.8862744 Thejaswini, S., & Indupriya, C. (2019). Big Data Security Issues and Natural Language Processing. Proceedings of the Third International Conference on Trends in Electronics and Informatics ICOEI 2019.