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Transformer-based Summarization and Sentiment Analysis of SEC 10-K Annual Reports for Company Performance Prediction
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2022 | HICSS | Citations: 0
Authors: Hsieh, Hsin-Ting; Hristova, Diana

Abstract: Annual reports published by companies contain important insights regarding their ... Expand

Abstract: Annual reports published by companies contain important insights regarding their performance and are often analyzed in a manual, subjective manner. We address this point by combining the streams of research on text summarization and topic modelling with the one on sentiment analysis. Our approach consists of the steps of text summarization using BERTSUMEXT, topic modelling with LDA, sentiment analysis with FinBERT, and performance prediction with Decision Trees and Random Forest. The result provides decision makers with an interpretable and condensed representation of the content of annual reports, together with its relationship to future company performance. We evaluate our approach on 10-K reports, demonstrating both its interpretability for analysts and explanatory power regarding future company performance. Collapse

Semantic filters: pre-trained language model precision score

Topics: organizational productivity data breach cloud computing Python explainable artificial intelligence

Methods: sentiment analysis topic model machine learning BERT decision tree classification
SymptomID: A Framework for Rapid Symptom Identification in Pandemics Using News Reports
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2021 | ACM Transactions on Management Information Systems | Citations: 1
Authors: Gu, Kang; Vosoughi, Soroush; Prioleau, Temiloluwa

Abstract: The ability to quickly learn fundamentals about a new infectious disease, such a ... Expand

Abstract: The ability to quickly learn fundamentals about a new infectious disease, such as how it is transmitted, the incubation period, and related symptoms, is crucial in any novel pandemic. For instance, rapid identification of symptoms can enable interventions for dampening the spread of the disease. Traditionally, symptoms are learned from research publications associated with clinical studies. However, clinical studies are often slow and time intensive, and hence delays can have dire consequences in a rapidly spreading pandemic like we have seen with COVID-19. In this article, we introduce SymptomID, a modular artificial intelligence–based framework for rapid identification of symptoms associated with novel pandemics using publicly available news reports. SymptomID is built using the state-of-the-art natural language processing model (Bidirectional Encoder Representations for Transformers) to extract symptoms from publicly available news reports and cluster-related symptoms together to remove redundancy. Our proposed framework requires minimal training data, because it builds on a pre-trained language model. In this study, we present a case study of SymptomID using news articles about the current COVID-19 pandemic. Our COVID-19 symptom extraction module, trained on 225 articles, achieves an F1 score of over 0.8. SymptomID can correctly identify well-established symptoms (e.g., “fever” and “cough”) and less-prevalent symptoms (e.g., “rashes,” “hair loss,” “brain fog”) associated with the novel coronavirus. We believe this framework can be extended and easily adapted in future pandemics to quickly learn relevant insights that are fundamental for understanding and combating a new infectious disease. Collapse

Semantic filters: pre-trained language model precision score

Topics: pandemic artificial intelligence application programming interface

Methods: BERT cluster analysis named entity recognition literature sample case study

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Transformer-based Summarization and Sentiment Analysis of SEC 10-K Annual Reports for Company Performance Prediction

SymptomID: A Framework for Rapid Symptom Identification in Pandemics Using News Reports