Identifying patient experience from online resources via sentiment analysis and topic modelling

Mohammed Bahja; Mark Lycett

doi:10.1145/3006299.3006335

Identifying patient experience from online resources via sentiment analysis and topic modelling

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Original language	English
Title of host publication	Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016
Publisher	Association for Computing Machinery, Inc
Pages	94-99
Number of pages	6
ISBN (Electronic)	9781450346177
DOIs	https://doi.org/10.1145/3006299.3006335
Publication status	Published - 6 Dec 2016
Event	3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016 - Shanghai, China Duration: 6 Dec 2016 → 9 Dec 2016

Conference

Conference	3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016
Country/Territory	China
City	Shanghai
Period	6/12/16 → 9/12/16

Keywords

Component
NHS Choices
Opinion mining
Patient experience
Patient feedback
Sentiment analysis
Text mining

Access to Document

10.1145/3006299.3006335

Accepted ManuscriptAccepted author manuscript, 576 KB

Cite this

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title = "Identifying patient experience from online resources via sentiment analysis and topic modelling",

keywords = "Component, NHS Choices, Opinion mining, Patient experience, Patient feedback, Sentiment analysis, Text mining",

author = "Mohammed Bahja and Mark Lycett",

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day = "6",

doi = "10.1145/3006299.3006335",

language = "English",

pages = "94--99",

booktitle = "Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016",

publisher = "Association for Computing Machinery, Inc",

note = "3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

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Bahja, M & Lycett, M 2016, Identifying patient experience from online resources via sentiment analysis and topic modelling. in Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016. Association for Computing Machinery, Inc, pp. 94-99, 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016, Shanghai, China, 6/12/16. https://doi.org/10.1145/3006299.3006335

Identifying patient experience from online resources via sentiment analysis and topic modelling. / Bahja, Mohammed; Lycett, Mark.
Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016. Association for Computing Machinery, Inc, 2016. p. 94-99.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Identifying patient experience from online resources via sentiment analysis and topic modelling

AU - Bahja, Mohammed

AU - Lycett, Mark

PY - 2016/12/6

Y1 - 2016/12/6

KW - Component

KW - NHS Choices

KW - Opinion mining

KW - Patient experience

KW - Patient feedback

KW - Sentiment analysis

KW - Text mining

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U2 - 10.1145/3006299.3006335

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M3 - Conference contribution

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EP - 99

BT - Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016

PB - Association for Computing Machinery, Inc

T2 - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016

Y2 - 6 December 2016 through 9 December 2016

ER -

Identifying patient experience from online resources via sentiment analysis and topic modelling

Conference

Keywords

Access to Document

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