Interpretable business survival prediction
| cam.depositDate | 2022-06-23 | |
| cam.issuedOnline | 2022-01-19 | |
| cam.orpheus.success | Mon Jun 27 07:19:08 BST 2022 - Embargo updated | |
| dc.contributor.author | Vallapuram, AK | |
| dc.contributor.author | Nanda, N | |
| dc.contributor.author | Kwon, YD | |
| dc.contributor.author | Hui, P | |
| dc.contributor.orcid | Kwon, Young Dae [0000-0002-5216-9057] | |
| dc.date.accessioned | 2022-06-24T23:30:26Z | |
| dc.date.available | 2022-06-24T23:30:26Z | |
| dc.date.issued | 2021-11-08 | |
| dc.date.updated | 2022-06-22T23:42:55Z | |
| dc.description.abstract | The survival of a business is undeniably pertinent to its success. A key factor contributing to its continuity depends on its customers. The surge of location-based social networks such as Yelp, Diangping, and Foursquare has paved the way for leveraging user-generated content on these platforms to predict business survival. Prior works in this area have developed several quantitative features to capture geography and user mobility among businesses. However, the development of qualitative features is minimal. In this work, we thus perform extensive feature engineering across four feature sets, namely, geography, user mobility, business attributes, and linguistic modelling to develop classifiers for business survival prediction. We additionally employ an interpretability framework to generate explanations and qualitatively assess the classifiers' predictions. Experimentation among the feature sets reveals that qualitative features including business attributes and linguistic features have the highest predictive power, achieving AUC scores of 0.72 and 0.67, respectively. Furthermore, the explanations generated by the interpretability framework demonstrate that these models can potentially identify the reasons from review texts for the survival of a business. | |
| dc.identifier.doi | 10.17863/CAM.85762 | |
| dc.identifier.isbn | 9781450391283 | |
| dc.identifier.uri | https://www.repository.cam.ac.uk/handle/1810/338353 | |
| dc.language.iso | eng | |
| dc.publisher | ACM | |
| dc.publisher.department | Department of Computer Science and Technology | |
| dc.publisher.url | http://dx.doi.org/10.1145/3487351.3488353 | |
| dc.rights | All Rights Reserved | |
| dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
| dc.subject | 46 Information and Computing Sciences | |
| dc.subject | 4406 Human Geography | |
| dc.subject | 44 Human Society | |
| dc.title | Interpretable business survival prediction | |
| dc.type | Conference Object | |
| prism.endingPage | 106 | |
| prism.publicationDate | 2021 | |
| prism.publicationName | Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 | |
| prism.startingPage | 99 | |
| pubs.conference-name | ASONAM '21: International Conference on Advances in Social Networks Analysis and Mining | |
| pubs.licence-display-name | Apollo Repository Deposit Licence Agreement | |
| pubs.licence-identifier | apollo-deposit-licence-2-1 | |
| rioxxterms.version | AM | |
| rioxxterms.versionofrecord | 10.1145/3487351.3488353 |
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