Extracting Keywords from Open-Ended Business Survey Questions (1808.10685v3)
Abstract: Open-ended survey data constitute an important basis in research as well as for making business decisions. Collecting and manually analysing free-text survey data is generally more costly than collecting and analysing survey data consisting of answers to multiple-choice questions. Yet free-text data allow for new content to be expressed beyond predefined categories and are a very valuable source of new insights into people's opinions. At the same time, surveys always make ontological assumptions about the nature of the entities that are researched, and this has vital ethical consequences. Human interpretations and opinions can only be properly ascertained in their richness using textual data sources; if these sources are analyzed appropriately, the essential linguistic nature of humans and social entities is safeguarded. NLP offers possibilities for meeting this ethical business challenge by automating the analysis of natural language and thus allowing for insightful investigations of human judgements. We present a computational pipeline for analysing large amounts of responses to open-ended questions in surveys and extract keywords that appropriately represent people's opinions. This pipeline addresses the need to perform such tasks outside the scope of both commercial software and bespoke analysis, exceeds the performance to state-of-the-art systems, and performs this task in a transparent way that allows for scrutinising and exposing potential biases in the analysis. Following the principle of Open Data Science, our code is open-source and generalizable to other datasets.
- A keyword extraction method from Twitter messages represented as graphs. Applied Mathematics and Computation, 240:308–325, 2014.
- R Harald Baayen. Word frequency distributions. Kluwer, Dordrecht, 2001.
- An overview of graph-based keyword extraction methods and approaches. Journal of information and organizational sciences, 39(1):1–20, 2015.
- Natural Language Processing with Python. O’Reilly, Sebastopol, CA, 2009.
- Kenneth Ward Church. Emerging trends: I did it, I did it, I did it, but…. Natural Language Engineering, 23(3):473–480, 2017.
- Wayne W Daniel. Spearman rank correlation coefficient. Applied nonparametric statistics, 2nd ed. PWS-Kent, Boston, pages 358–365, 1990.
- Natural language processing: state of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine. Journal of biomedical informatics, 46(5):765–773, 2013.
- Stephen Gorard. Quantitative methods in social science research. Continuum, London, 2003.
- Automatic Keyphrase Extraction: A Survey of the State of the Art. In ACL (1), pages 1262–1273, 2014.
- Martin Heidegger. On the way to language. Harper & Row, 1959.
- Dominik Heil. Ontological fundamentals for ethical management: Heidegger and the corporate world, volume 35. Springer Science & Business Media, 2011.
- A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model. Scientometrics, 114(3):1031–1068, 2018.
- Keith Johnson. Quantitative Methods in Linguistics. Blackwell, Malden, MA., 2008.
- Quantitative classification of Indo-European languages. Language, 13(2):83–103, 1937.
- Keyword extraction from emails. Natural Language Engineering, 23(2):295–317, 2017.
- Unsupervised approaches for automatic keyword extraction using meeting transcripts. In Proceedings of human language technologies: The 2009 annual conference of the North American chapter of the association for computational linguistics, pages 620–628. Association for Computational Linguistics, 2009.
- Christopher D Manning. Computational linguistics and deep learning. Computational Linguistics, 41(4):701–707, 2015.
- Foundations of statistical natural language processing, volume 999. MIT Press, 1999.
- Text mining infrastructure in R. Journal of statistical software, 25(5):1–54, 2008.
- The case for qualitative research. Academy of management review, 5(4):491–500, 1980.
- Log-likelihood and odds ratio: Keyness statistics for different purposes of keyword analysis. Corpus Linguistics and Linguistic Theory, 14(1):133–167, 2018.
- Walter Powell. Strategy without ontology. Strategic Management Journal, 24:285–291, 2003.
- Topia.termextract, 2009. Computer software.
- Automatic keyword extraction from individual documents. In M. W. Berry and J. Kogan, editors, Text mining: applications and theory, pages 1–20. John Wiley & Sons, Chichester, UK, 2010.
- Keyword and keyphrase extraction techniques: a literature review. International Journal of Computer Applications, 109(2), 2015.
- Karen Spärck Jones. A statistical interpretation of term specificity and its application in retrieval. Journal of documentation, 28(1):11–21, 1972.
- Karen Spärck Jones. IDF term weighting and IR research lessons. Journal of documentation, 60(5):521–523, 2004.
- Ludwig Wittgenstein. Philosophical investigations (GEM Anscombe, trans.). Oxford: Blackwell, 1953.
- Adapted TextRank for term extraction: A generic method of improving automatic term extraction algorithms. Procedia Computer Science, 137:102–108, 2018.