dc.contributor.author |
Devine, Peter |
|
dc.contributor.author |
Koh, Yun Sing |
|
dc.contributor.author |
Blincoe, Kelly |
|
dc.date.accessioned |
2021-12-06T02:38:59Z |
|
dc.date.available |
2021-12-06T02:38:59Z |
|
dc.date.issued |
2021-9 |
|
dc.identifier.citation |
2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), 2021, pp. 87-95 |
|
dc.identifier.isbn |
9781665418980 |
|
dc.identifier.issn |
1090-705X |
|
dc.identifier.uri |
https://hdl.handle.net/2292/57648 |
|
dc.description.abstract |
User feedback on software products has been shown to be useful for development and can be exceedingly abundant online. Many approaches have been developed to elicit requirements in different ways from this large volume of feedback, including the use of unsupervised clustering, underpinned by text embeddings. Methods for embedding text can vary significantly within the literature, highlighting the lack of a consensus as to which approaches are best able to cluster user feedback into requirements relevant groups. This work proposes a methodology for comparing text embeddings of user feedback using existing labelled datasets. Using 7 diverse datasets from the literature, we apply this methodology to evaluate both established text embedding techniques from the user feedback analysis literature (including topic modelling and word embeddings) as well as text embeddings from state of the art deep text embedding models. Results demonstrate that text embeddings produced by state of the art models, most notably the Universal Sentence Encoder (USE), group feedback with similar requirements relevant characteristics together better than other evaluated techniques across all seven datasets. These results can help researchers select appropriate embedding techniques when developing future unsupervised clustering approaches within user feedback analysis. |
|
dc.publisher |
IEEE |
|
dc.relation.ispartof |
2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) |
|
dc.relation.ispartofseries |
2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights |
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/#accepted |
|
dc.title |
Evaluating Unsupervised Text Embeddings on Software User Feedback |
|
dc.type |
Conference Item |
|
dc.identifier.doi |
10.1109/rew53955.2021.00020 |
|
pubs.begin-page |
87 |
|
pubs.volume |
2021-September |
|
dc.date.updated |
2021-11-14T20:12:04Z |
|
dc.rights.holder |
Copyright: IEEE |
en |
pubs.end-page |
95 |
|
pubs.finish-date |
2021-9-24 |
|
pubs.publication-status |
Published |
|
pubs.start-date |
2021-9-20 |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.elements-id |
873426 |
|
dc.identifier.eissn |
2332-6441 |
|