Skip to main content

Introduction to Text Analytics

  • Chapter
  • First Online:
Practical Text Analytics

Part of the book series: Advances in Analytics and Data Science ((AADS,volume 2))

  • 4231 Accesses

Abstract

In this chapter we define text analytics, discuss its origins, cover its current usage, and show its value to businesses. The chapter describes examples of current text analytics uses to demonstrate the wide array of real-world impacts. Finally, we present a process road map as a guide to text analytics and to the book.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Agency breaking law by mining social media. (2017, 12). USA Today, 146, 14–15.

  2. 2.

    http://www.businessinsider.com/analytics-firm-crimson-hexagon-uses-social-media-to-predict-stock-movements-2017-4

  3. 3.

    https://www.indeed.com/

References

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002). Latent Dirichlet allocation. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems (pp. 601–608). Cambridge: MIT Press.

    Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

    Google Scholar 

  • Bolasco, S., Canzonetti, A., Capo, F., Della Ratta-Rinaldi, F., & Singh, B. (2005). Understanding text mining: A pragmatic approach. In Knowledge mining (pp. 31–50). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Bose, R. (2009). Advanced analytics: Opportunities and challenges. Industrial Management & Data Systems, 109(2), 155–172.

    Article  Google Scholar 

  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36, 1165–1188.

    Article  Google Scholar 

  • Dave, K., Lawrence, S., & Pennock, D. M. (2003, May). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International Conference on World Wide Web (pp. 519–528). ACM.

    Google Scholar 

  • Davenport, T. H. (2013). Analytics 3.0. Boston: Harvard Business Review.

    Google Scholar 

  • Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391.

    Article  Google Scholar 

  • Dörre, J., Gerstl, P., & Seiffert, R. (1999). Text mining: Finding nuggets in mountains of textual data. In Proceedings of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 398–401). ACM.

    Google Scholar 

  • Dumais, S. T., Furnas, G. W., Landauer, T. K., & Deerwester, S. (1988). Using latent semantic analysis to improve information retrieval. In Proceedings of CHI’88: Conference on Human Factors in Computing (pp. 281–285). New York: ACM.

    Google Scholar 

  • Fan, W., Wallace, L., Rich, S., & Zhang, Z. (2006). Tapping the power of text mining. Communications of the ACM, 49(9), 76–82. https://doi.org/10.1145/1151030.1151032.

    Article  Google Scholar 

  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37.

    Google Scholar 

  • Feldman, R., & Dagan, I. (1995, August). Knowledge discovery in textual databases (KDT). KDD, 95, 112–117.

    Google Scholar 

  • Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1992). Knowledge discovery in databases: An overview. AI Magazine, 13(3), 57.

    Google Scholar 

  • Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60–76.

    Article  Google Scholar 

  • Hearst, M. A. (1999a, June). Untangling text data mining. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics (pp. 3–10). Association for Computational Linguistics.

    Google Scholar 

  • Hearst, M. A. (1999b). The use of categories and clusters for organizing retrieval results. In Natural language information retrieval (pp. 333–374). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Hearst, M. (2003). What is text mining. UC Berkeley: SIMS.

    Google Scholar 

  • Hofmann, T. (1999, July). Probabilistic latent semantic analysis. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (pp. 289–296).

    Google Scholar 

  • IBM. (2011, November 15). The 2011 IBM tech trends report: The clouds are rolling in….is your business ready?http://www.ibm.com/developerworks/techtrendsreport

  • Krippendorff, K. (2012). Content analysis: An introduction to its methodology. Thousand Oaks: Sage.

    Google Scholar 

  • LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.

    Google Scholar 

  • Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.

    Article  Google Scholar 

  • Miner, G., et al. (2012). Practical text mining and statistical analysis for non-structured text data applications. Amsterdam: Academic Press.

    Google Scholar 

  • Müller, O., Junglas, I., Debortoli, S., & vom Brocke, J. (2016). Using text analytics to derive customer service management benefits from unstructured data. MIS Quarterly Executive, 15(4), 243–258.

    Google Scholar 

  • Nagarkar, S. P., & Kumbhar, R. (2015). Text mining: An analysis of research published under the subject category ‘information science library science’ in web of science database during 1999–2013. Library Review, 64(3), 248–262.

    Article  Google Scholar 

  • Nasukawa, T., & Yi, J. (2003, October). Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture (pp. 70–77). ACM.

    Google Scholar 

  • Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys (CSUR), 34(1), 1–47.

    Article  Google Scholar 

  • Talib, R., Hanif, M. K., Ayesha, S., & Fatima, F. (2016). Text mining: Techniques, applications and issues. International Journal of Advanced Computer Science & Applications, 1(7), 414–418.

    Google Scholar 

  • Ur-Rahman, N., & Harding, J. A. (2012). Textual data mining for industrial knowledge management and text classification: A business oriented approach. Expert Systems with Applications, 39(5), 4729–4739.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Anandarajan, M., Hill, C., Nolan, T. (2019). Introduction to Text Analytics. In: Practical Text Analytics. Advances in Analytics and Data Science, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-95663-3_1

Download citation

Publish with us

Policies and ethics