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 proceedingConference contribution

Published

Standard

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 proceedingConference contribution

Harvard

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

APA

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 (pp. 94-99). Association for Computing Machinery, Inc. https://doi.org/10.1145/3006299.3006335

Vancouver

Bahja M, Lycett M. 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. 2016. p. 94-99 https://doi.org/10.1145/3006299.3006335

Author

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

BibTeX

@inproceedings{1de5e1414f0b4758825e411ec9a55c95,
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",
year = "2016",
month = dec,
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",

}

RIS

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

UR - http://www.scopus.com/inward/record.url?scp=85013223085&partnerID=8YFLogxK

U2 - 10.1145/3006299.3006335

DO - 10.1145/3006299.3006335

M3 - Conference contribution

AN - SCOPUS:85013223085

SP - 94

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 -