Clustering-Based Online News Topic Detection and Tracking Through Hierarchical Bayesian Nonparametric Models

Fan, Wentao; Guo, Zhiyan; Bouguila, Nizar; Hou, Wen-Juan

doi:10.1145/3404835.3462982

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…This is critical for the financial industry, because prior risk intervention is more important than risk prediction. It's also the unique advantage of BN; (2) BN has the advantage of reasoning under uncertainty and updating its prediction according to new observations (Fan et al ., 2021). With the passage of time, the variables affecting credit risk are constantly changing.…”

Section: Literature Reviewmentioning

confidence: 99%

A decision support method for credit risk based on the dynamic Bayesian network

Lu,

Wu,

Dong

et al. 2023

IMDS

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PurposeCredit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.Design/methodology/approachFirst, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.FindingsCompared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.Originality/valueThe authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

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Section: Literature Reviewmentioning

confidence: 99%

A decision support method for credit risk based on the dynamic Bayesian network

Lu,

Wu,

Dong

et al. 2023

IMDS

View full text Add to dashboard Cite

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“…TDT approaches (e.g. [1,5,18,24]) focus on identifying topical relationships between the documents to automatically detect topical clusters (i.e., topics), and to track follow-up documents that are related to such topics. These topics are often a group of many related events [24].…”

Section: Related Workmentioning

confidence: 99%

Effective Hierarchical Information Threading Using Network Community Detection

Narvala

McDonald

Ounis

2023

Lecture Notes in Computer Science

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With the tremendous growth in the volume of information produced online every day (e.g. news articles), there is a need for automatic methods to identify related information about events as the events evolve over time (i.e., information threads). In this work, we propose a novel unsupervised approach, called HINT, which identifies coherent Hierarchical Information Threads. These threads can enable users to easily interpret a hierarchical association of diverse evolving information about an event or discussion. In particular, HINT deploys a scalable architecture based on network community detection to effectively identify hierarchical links between documents based on their chronological relatedness and answers to the 5W1H questions (i.e., who, what, where, when, why & how). On the NewSHead collection, we show that HINT markedly outperforms existing state-of-the-art approaches in terms of the quality of the identified threads. We also conducted a user study that shows that our proposed network-based hierarchical threads are significantly (p < 0.05) preferred by users compared to cluster-based sequential threads.

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“…The first stream is that of online topic models. which incorporate new data incrementally [3,6]. In [3], the authors propose Online-LDA, a version of LDA able to update itself with new documents without having to access to previously processed documents.…”

Section: Topic Trackingmentioning

confidence: 99%

“…They performed their experiments on the NIPS and Reuters-21578 datasets. Similarly in [6], the authors propose a model that can dynamically decide the right number of topics in an online fashion. They performed their experiments on the the 20 Newsgroup and the TDT-2 datasets.…”

Section: Topic Trackingmentioning

confidence: 99%

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Using Meaning Instead of Words to Track Topics

Poumay

Ittoo

2022

Natural Language Processing and Information Systems

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The ability to monitor the evolution of topics over time is extremely valuable for businesses. Currently, all existing topic tracking methods use lexical information by matching word usage. However, no studies has ever experimented with the use of semantic information for tracking topics. Hence, we explore a novel semantic-based method using word embeddings. Our results show that a semantic-based approach to topic tracking is on par with the lexical approach but makes different mistakes. This suggest that both methods may complement each other.

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Clustering-Based Online News Topic Detection and Tracking Through Hierarchical Bayesian Nonparametric Models

Cited by 10 publications

References 26 publications

A decision support method for credit risk based on the dynamic Bayesian network

A decision support method for credit risk based on the dynamic Bayesian network

Effective Hierarchical Information Threading Using Network Community Detection

Using Meaning Instead of Words to Track Topics

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