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- Fischbach, Jannik, et al.
(författare)
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Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data : NLP Approach and Tool
- 2023
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Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350324457 ; , s. 2823-2830
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Konferensbidrag (refereegranskat)abstract
- [Context:] Society increasingly values sustainable corporate behaviour, impacting corporate reputation and customer trust. Hence, companies regularly publish sustainability reports to shed light on their impact on environmental, social, and governance (ESG) factors. [Problem:] Sustainability reports are written by companies and therefore considered a company-controlled source. Contrarily, studies reveal that non-corporate channels (e.g., media coverage) represent the main driver for ESG transparency. However, analysing media coverage regarding ESG factors is challenging since (1) the amount of published news articles grows daily, (2) media coverage data does not necessarily deal with an ESG-relevant topic, meaning that it must be carefully filtered, and (3) the majority of media coverage data is unstructured. [Research Goal:] We aim to automatically extract ESG-relevant information from textual media reactions to calculate an ESG score for a given company. Our goal is to reduce the cost of ESG data collection and make ESG information available to the general public. [Contribution:] Our contributions are three-fold: First, we publish a corpus of 432,411 news headlines annotated as being environmental-, governance-, social-related, or ESG-irrelevant. Second, we present our tool-supported approach called ESG-Miner, capable of automatically analysing and evaluating corporate ESG performance headlines. Third, we demonstrate the feasibility of our approach in an experiment and apply the ESG-Miner on 3000 manually labelled headlines. Our approach correctly processes 96.7% of the headlines and shows great performance in detecting environmental-related headlines and their correct sentiment. © 2023 IEEE.
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| 2. |
- Sahoo, Kshira Sagar, et al.
(författare)
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Combining block bootstrap with exponential smoothing for reinforcing non-emergency urban service prediction
- 2023
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Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350324457 ; , s. 4335-4343
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Konferensbidrag (refereegranskat)abstract
- In major urban cities, government authorities have developed various service-requesting systems to report non-emergency public issues related to urban rare events such as noise, blocked driveways, illegal parking, etc. For certain events, request volumes can surge significantly, and timely response depends on accurate prediction. In this paper, we investigate how long it takes to resolve service requests by the agencies. This paper introduces NERPS, a non-emergency response system designed to forecast service request response time. Leveraging urban data, the model establishes connections between historical and future response times. In time series data, applying boot-strapping on the reminder component for generating synthetic data with original time series before fitting the model has been viewed to be effective. The NERPS integrates Holt-Winters with the Moving Block Bootstrap (MBB+HW) model for forecasting the service requests in the NYC dataset. Proposed model forecasts to generate 100-time series values and final prediction obtained by averaging the forecast set. The optimal block size is estimated via the flat-top lag windows technique. This research extends beyond prior studies by comparing the forecasting performance of proposed statistical methods with MI/DL approaches on complex and nonlinear time series data. We consider SARIMA, ARIMA, FB-Prophet, linear regression and basic LSTM as baseline models for response time forecasting and compare the proposed model with multistep ahead point forecasts. The results show that in most cases, the NERPS achieves low RMSE, MAE and Relative Errors among top complaint types and agencies.
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| 3. |
- Tsiporkova, Elena, et al.
(författare)
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Mitigating Concept Drift in Distributed Contexts with Dynamic Repository of Federated Models
- 2023
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Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350324457 ; , s. 2690-2699
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a novel federated learning methodology, called FedRepo, that copes with concept drift issues in a statistically heterogeneous distributed learning environment. The proposed horizontal federated learning methodology, based on random forest (RF), can be used for collaborative training and maintenance of a dynamic repository of federated RF models, each one customized to a group of clients/devices. The clients are grouped together if their performance patterns with respect to the global RF model are similar. The performance of the customized RF global models is continuously monitored during the inference phase and the repository is accordingly adapted to mitigate the detected concept drift. The proposed methodology is studied and evaluated against an electricity consumption forecasting use case. The evaluation results demonstrate clearly that the proposed methodology is able to deal with concept drift issues in an efficient and adequate fashion without compromising the overall performance of the distributed environment. © 2023 IEEE.
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