Mitic, Peter;
(2024)
Is Positive Sentiment Missing in Corporate Reputation?
In:
Proceedings of the 13th International Conference on Data Science, Technology and Applications.
(pp. pp. 71-81).
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Abstract
The value of a perceived negative bias is quantified in the context of corporate reputation time series, derived by exhaustive data mining and automated natural language processing. Two methods of analysis are proposed: State-Space using a Kalman filter time series with a Normal distribution profile, and Forward Filtering Backward Sampling for those without. Normality tests indicate that approximately 92% of corporate reputation time series do fit the Normal profile. The results indicate that observed positive reputation profiles should be boosted by approximately 4% to account for negative bias. Examination of the observed balance between negative and positive sentiment in reputation time series indicates dependence on the sentiment calculation method, and region. Positive sentiment predominates in the US, Japan and parts of Western Europe, but not in the UK or in Hong Kong/China.
| Type: | Proceedings paper |
|---|---|
| Title: | Is Positive Sentiment Missing in Corporate Reputation? |
| Event: | 13th International Conference on Data Science, Technology and Applications |
| Location: | Dijon, France |
| Dates: | 9th-11th July 2024 |
| ISBN-13: | 978-989-758-707-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.5220/0012763100003756 |
| Publisher version: | http://dx.doi.org/10.5220/0012763100003756 |
| Language: | English |
| Additional information: | © The Author 2024. Original content in this paper is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | State-Space, Kalman filter, Kalman, Forward Filtering Backward Sampling, FFBS, MCMC, TNA, reputation, sentiment, missing sentiment, missing positive sentiment, negative bias |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10194783 |
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