TY  - GEN
AV  - public
Y1  - 2024///
SN  - 2184-285X
TI  - Is Positive Sentiment Missing in Corporate Reputation?
UR  - http://dx.doi.org/10.5220/0012763100003756
EP  - 81
ID  - discovery10194783
N2  - 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.
SP  - 71
N1  - © 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/).
A1  - Mitic, Peter
PB  - SCITEPRESS - Science and Technology Publications
KW  - State-Space
KW  -  Kalman filter
KW  -  Kalman
KW  -  Forward Filtering Backward Sampling
KW  -  FFBS
KW  -  MCMC
KW  -  TNA
KW  -  reputation
KW  -  sentiment
KW  -  missing sentiment
KW  -  missing positive sentiment
KW  -  negative bias
ER  -