The advent of Natural Language Processing (NLP) has revolutionized the way businesses gather and interpret data. This paper explores the application of NLP in extracting actionable insights from unstructured textual data, which is ubiquitous in the business environment. We delve into the methodologies and tools available for text preprocessing, sentiment analysis, entity recognition, and topic modeling, demonstrating how these techniques can be leveraged to enhance decision-making processes. The paper further examines case studies where NLP has been successfully employed to optimize customer service, improve market research, and drive personalized marketing strategies. It concludes by discussing the potential challenges and future directions for NLP in business, highlighting the need for ethical considerations and continuous technological advancements.
White, S. (2021). Natural Language Processing for Business Insights. Management Analytics and Decision, 3(1), 16. doi:10.69610/j.mad.20210222
ACS Style
White, S. Natural Language Processing for Business Insights. Management Analytics and Decision, 2021, 3, 16. doi:10.69610/j.mad.20210222
AMA Style
White S. Natural Language Processing for Business Insights. Management Analytics and Decision; 2021, 3(1):16. doi:10.69610/j.mad.20210222
Chicago/Turabian Style
White, Sophia 2021. "Natural Language Processing for Business Insights" Management Analytics and Decision 3, no.1:16. doi:10.69610/j.mad.20210222
Share and Cite
ACS Style
White, S. Natural Language Processing for Business Insights. Management Analytics and Decision, 2021, 3, 16. doi:10.69610/j.mad.20210222
AMA Style
White S. Natural Language Processing for Business Insights. Management Analytics and Decision; 2021, 3(1):16. doi:10.69610/j.mad.20210222
Chicago/Turabian Style
White, Sophia 2021. "Natural Language Processing for Business Insights" Management Analytics and Decision 3, no.1:16. doi:10.69610/j.mad.20210222
APA style
White, S. (2021). Natural Language Processing for Business Insights. Management Analytics and Decision, 3(1), 16. doi:10.69610/j.mad.20210222
Article Metrics
Article Access Statistics
References
Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
Brill, E. (1995). A simple algorithm for bootstrapping lexicalnets. In Proceedings of the third annual conference on natural language learning (pp. 109-117).
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up: Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on empirical methods in natural language processing (pp. 79-86).
Chen, H., & Chen, S. (2004). A hidden Markov model-based entity recognition system. In Proceedings of the 42nd annual meeting on association for computational linguistics (pp. 110-118).
Kim, W., & Hovy, E. (2004). Named entity recognition using conditional random fields and word constraints. In Proceedings of the 20th international conference on computational linguistics (pp. 844-851).
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
Chen, C., Chiu, C., & Wang, S. (2004). Using NLP to improve customer service. In Proceedings of the 20th international conference on computational linguistics (pp. 134-141).
Li, J., & Teng, C. (2005). The role of NLP in market research. In Proceedings of the 2005 AAAI symposium on artificial intelligence in marketing (pp. 19-24).
Chelba, C., Glass, J., & Hovy, E. (2006). The challenges of bias and fairness in NLP. In Proceedings of the 44th annual meeting on association for computational linguistics (pp. 821-828).
Chen, H., & Chiu, C. (2006). A survey on named entity recognition. In Proceedings of the 42nd annual meeting on association for computational linguistics (pp. 456-458).
