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Constructing a sentiment analysis model for LibQUAL+ comments

Michael Thomas Moore (School of Information Studies, McGill University, Montreal, Canada)

Performance Measurement and Metrics

ISSN: 1467-8047

Article publication date: 10 April 2017

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Abstract

Purpose

The purpose of this paper is to establish a data mining model for performing sentiment analysis on open-ended qualitative LibQUAL+ comments, providing a further method for year-to-year comparison of user satisfaction, both of the library as a whole and individual topics.

Design/methodology/approach

A training set of 514 comments, selected at random from five LibQUAL+ survey responses, was manually reviewed and labeled as having a positive or negative sentiment. Using the open-source RapidMiner data mining platform, those comments provided the framework for creating library-specific positive and negative word vectors to power the sentiment analysis model. A further process was created to help isolate individual topics within the larger comments, allowing for more nuanced sentiment analysis.

Findings

Applied to LibQUAL+ comments for a Canadian mid-sized academic research library, the model suggested a fairly even distribution of positive and negative sentiment in overall comments. When filtering comments into affect of service, information control and library as place, the three dimensions’ relative polarity mirrored the results of the quantitative LibQUAL+ questions, with highest scores for affect of service and lowest for library as place.

Practical implications

The sentiment analysis model provides a complementary tool to the LibQUAL+ quantitative results, allowing for simple, time-efficient, year-to-year analysis of open-ended comments. Furthermore, the process provides the means to isolate specific topics based on specified keywords, allowing individual institutions to tailor results for more in-depth analysis.

Originality/value

To best account for library-specific terminology and phrasing, the sentiment model was created using LibQUAL+ open-ended comments as the foundation for the sentiment model’s classification process. The process also allows individual topics, chosen to meet individual library needs, to be isolated and independently analyzed, providing more precise examination.

Keywords

Acknowledgements

The author would like to thank C. Colleen Cook and Tim Moore for their feedback and support on the paper and the library staff who processed and made available the LibQUAL+ results and comments.

Citation

Moore, M.T. (2017), "Constructing a sentiment analysis model for LibQUAL+ comments", Performance Measurement and Metrics, Vol. 18 No. 1, pp. 78-87. https://doi.org/10.1108/PMM-07-2016-0031

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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