A happy visitor or not? Training SENSIOM on museum visitors-related datasets for sentiment analysis (5)

Demonstration

Friday, April 05, 2019: 1:00pm - 2:00pm - Constitution: Demonstrations 3

Georgios Papaioannou, UCL Qatar, Qatar

This talk will present the processes we follow to train the SENSIOM data dashboard for sentiment analysis. The SENSION has been one of the outcomes of the Museum Big Data Research Project in Qatar, initiated in 2017 at University College London in Qatar. It is a dynamic multi-functional data dashboard on Museum Big Data to serve research needs on Negative/Neutral/Positive sentiment analysis and analysis on museum visitors’ views on different museum-related topics, such as price of tickets, queue, other assets and issues. Sentiment analysis is benchmarked against other museums and/or cultural institutions and hotspots. We train SENSION via museum visitors-related Big datasets to produce accurate and valid results.

Bibliography:
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Gerrard, David, Martin Sykora, and Thomas Jackson. 2017. “Social Media Analytics in Museums: Extracting Expressions of Inspiration.” Museum Management and Curatorship 32 (3): 232–50. https://doi.org/10.1080/09647775.2017.1302815.

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Liu, Bing. 2015. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions / Bing Liu, University of Illinois at Chicago. Cambridge: Cambridge University Press. http://dx.doi.org/10.1017/CBO9781139084789.

Papaioannou, Georgios. 2019 (in press). "Museum Big Data: perceptions and practices". In Big Data in Education: Pedagogy and Research, edited by Theodosia Prodromou, Springer (in press).

Pozzi, Federico Alberto, Elisabetta Fersini, Enza Messina, and Bing Liu. 2016. Sentiment Analysis in Social Networks. Morgan Kaufmann. http://proquest.safaribooksonline.com/9780128044384.

Villaespesa, Elena. 2013. “Diving into the Museum’s Social Media Stream. Analysis of the Visitor Experience in 140 Characters.” MW2013: Museums and the Web 2013 (blog). 2013. https://mw2013.museumsandtheweb.com/paper/diving-into-the-museums-social-media-stream/.