Leveraging State-of-the-Art Topic Modeling for News Impact Analysis on Financial Markets: A Comparative Study

News impact analysis has become a common task conducted by finance researchers, which involves reading and selecting news articles based on themes and sentiments, pairing news events and relevant stocks, and measuring the impact of selected news on stock prices. To facilitate more efficient news sel...

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Published inElectronics (Basel) Vol. 12; no. 12; p. 2605
Main Authors Chen, Weisi, Rabhi, Fethi, Liao, Wenqi, Al-Qudah, Islam
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
Analysis
Big data
Comparative studies
Computational linguistics
Computing time
Data mining
Deep learning
Dirichlet problem
Finance
Financial markets
Impact analysis
Language processing
Methods
Modelling
Natural language interfaces
Natural language processing
News
Securities markets
Semantics
Sentiment analysis
Social networks
Subject specialists
China
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Abstract News impact analysis has become a common task conducted by finance researchers, which involves reading and selecting news articles based on themes and sentiments, pairing news events and relevant stocks, and measuring the impact of selected news on stock prices. To facilitate more efficient news selection, topic modeling can be applied to generate topics out of a large number of news documents. However, there is very limited existing literature comparing topic models in the context of finance-related news impact analysis. In this paper, we compare three state-of-the-art topic models, namely Latent Dirichlet allocation (LDA), Top2Vec, and BERTopic, in a defined scenario of news impact analysis on financial markets, where 38,240 news articles with an average length of 590 words are analyzed. A service-oriented framework for news impact analysis called “News Impact Analysis” (NIA) is advocated to leverage multiple topic models and provide an automated and seamless news impact analysis process for finance researchers. Experimental results have shown that BERTopic performed best in this scenario, with minimal data preprocessing, the highest coherence score, the best interpretability, and reasonable computing time. In addition, a finance researcher was able to conduct the entire news impact analysis process, which validated the feasibility and usability of the NIA framework.
AbstractList News impact analysis has become a common task conducted by finance researchers, which involves reading and selecting news articles based on themes and sentiments, pairing news events and relevant stocks, and measuring the impact of selected news on stock prices. To facilitate more efficient news selection, topic modeling can be applied to generate topics out of a large number of news documents. However, there is very limited existing literature comparing topic models in the context of finance-related news impact analysis. In this paper, we compare three state-of-the-art topic models, namely Latent Dirichlet allocation (LDA), Top2Vec, and BERTopic, in a defined scenario of news impact analysis on financial markets, where 38,240 news articles with an average length of 590 words are analyzed. A service-oriented framework for news impact analysis called “News Impact Analysis” (NIA) is advocated to leverage multiple topic models and provide an automated and seamless news impact analysis process for finance researchers. Experimental results have shown that BERTopic performed best in this scenario, with minimal data preprocessing, the highest coherence score, the best interpretability, and reasonable computing time. In addition, a finance researcher was able to conduct the entire news impact analysis process, which validated the feasibility and usability of the NIA framework.
Audience Academic
Author Liao, Wenqi
Rabhi, Fethi
Chen, Weisi
Al-Qudah, Islam
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Cites_doi 10.1038/s42254-023-00581-4
10.1162/tacl_a_00078
10.1007/s10489-023-04452-4
10.1109/ACCESS.2021.3062875
10.1080/1331677X.2021.1941179
10.1007/978-3-030-98305-5_12
10.1016/j.ipm.2021.102708
10.1007/s11761-023-00358-8
10.1016/j.tourman.2019.01.002
10.1145/3507900
10.1177/0972150920957274
10.1080/19312458.2018.1430754
10.1016/j.is.2020.101582
10.3389/fsoc.2022.886498
10.3390/bdcc7010038
10.1007/s11280-022-01029-y
10.1016/j.is.2022.102131
10.1016/j.asoc.2022.109673
10.1016/j.compedu.2020.103855
10.1016/j.techfore.2021.121265
10.1016/j.knosys.2022.108742
10.1145/3520084.3520104
10.1109/ICOMET.2019.8673428
10.3390/bdcc6040130
10.1016/j.neucom.2021.05.103
10.1016/j.eswa.2023.119509
10.1186/s40537-019-0255-7
10.1126/sciadv.aaq1360
10.1186/s40537-022-00586-3
10.1080/00036846.2018.1564115
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References Poongodi (ref_30) 2021; 58
Churchill (ref_14) 2022; 54
Egger (ref_31) 2022; 7
ref_36
Eachempati (ref_9) 2022; 174
Ashtiani (ref_11) 2023; 217
ref_13
ref_35
ref_12
Bonifazi (ref_24) 2022; 9
Asmussen (ref_26) 2019; 6
ref_34
Vayansky (ref_15) 2020; 94
Bhat (ref_21) 2020; 41
Birhane (ref_38) 2023; 5
ref_19
Hu (ref_27) 2019; 72
ref_37
Abdelrazek (ref_16) 2023; 112
Ghasiya (ref_29) 2021; 9
Maier (ref_25) 2018; 12
Blei (ref_18) 2003; 3
Allen (ref_6) 2019; 51
TajMazinani (ref_4) 2022; 26
ref_23
ref_22
Yin (ref_32) 2022; 25
Lin (ref_10) 2022; 130
Lauriola (ref_5) 2022; 470
Chen (ref_28) 2020; 151
ref_3
Egger (ref_33) 2022; 77
Shahzad (ref_8) 2022; 35
Tabbakh (ref_1) 2022; 247
Gerlach (ref_20) 2018; 4
Gallagher (ref_17) 2017; 5
Chen (ref_2) 2023; 17
ref_7
References_xml – volume: 5
  start-page: 277
  year: 2023
  ident: ref_38
  article-title: Science in the age of large language models
  publication-title: Nat. Rev. Phys.
  doi: 10.1038/s42254-023-00581-4
– volume: 5
  start-page: 529
  year: 2017
  ident: ref_17
  article-title: Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
  publication-title: Trans. Assoc. Comput. Linguist.
  doi: 10.1162/tacl_a_00078
– ident: ref_34
  doi: 10.1007/s10489-023-04452-4
– volume: 9
  start-page: 36645
  year: 2021
  ident: ref_29
  article-title: Investigating COVID-19 News across Four Nations: A Topic Modeling and Sentiment Analysis Approach
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3062875
– volume: 35
  start-page: 2313
  year: 2022
  ident: ref_8
  article-title: Outbreak of epidemic diseases and stock returns: An event study of emerging economy
  publication-title: Econ. Res.-Ekon. Istraživanja
  doi: 10.1080/1331677X.2021.1941179
– ident: ref_35
  doi: 10.1007/978-3-030-98305-5_12
– volume: 58
  start-page: 102708
  year: 2021
  ident: ref_30
  article-title: Global cryptocurrency trend prediction using social media
  publication-title: Inf. Process. Manag.
  doi: 10.1016/j.ipm.2021.102708
– volume: 17
  start-page: 129
  year: 2023
  ident: ref_2
  article-title: A CEP-driven framework for real-time news impact prediction on financial markets
  publication-title: Serv. Oriented Comput. Appl.
  doi: 10.1007/s11761-023-00358-8
– volume: 72
  start-page: 417
  year: 2019
  ident: ref_27
  article-title: What do hotel customers complain about? Text analysis using structural topic model
  publication-title: Tour. Manag.
  doi: 10.1016/j.tourman.2019.01.002
– volume: 54
  start-page: 215
  year: 2022
  ident: ref_14
  article-title: The Evolution of Topic Modeling
  publication-title: ACM Comput. Surv.
  doi: 10.1145/3507900
– ident: ref_37
  doi: 10.1177/0972150920957274
– volume: 12
  start-page: 93
  year: 2018
  ident: ref_25
  article-title: Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology
  publication-title: Commun. Methods Meas.
  doi: 10.1080/19312458.2018.1430754
– volume: 94
  start-page: 101582
  year: 2020
  ident: ref_15
  article-title: A review of topic modeling methods
  publication-title: Inf. Syst.
  doi: 10.1016/j.is.2020.101582
– ident: ref_23
– volume: 7
  start-page: 80
  year: 2022
  ident: ref_31
  article-title: A Topic Modeling Comparison between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts
  publication-title: Front. Sociol.
  doi: 10.3389/fsoc.2022.886498
– ident: ref_13
  doi: 10.3390/bdcc7010038
– volume: 25
  start-page: 1067
  year: 2022
  ident: ref_32
  article-title: Sentiment analysis and topic modeling for COVID-19 vaccine discussions
  publication-title: World Wide Web
  doi: 10.1007/s11280-022-01029-y
– volume: 112
  start-page: 102131
  year: 2023
  ident: ref_16
  article-title: Topic modeling algorithms and applications: A survey
  publication-title: Inf. Syst.
  doi: 10.1016/j.is.2022.102131
– volume: 77
  start-page: 1234
  year: 2022
  ident: ref_33
  article-title: Identifying hidden semantic structures in Instagram data: A topic modelling comparison
  publication-title: Tour. Rev.
– volume: 130
  start-page: 109673
  year: 2022
  ident: ref_10
  article-title: Factors affecting text mining based stock prediction: Text feature representations, machine learning models, and news platforms
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2022.109673
– volume: 151
  start-page: 103855
  year: 2020
  ident: ref_28
  article-title: Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education
  publication-title: Comput. Educ.
  doi: 10.1016/j.compedu.2020.103855
– volume: 174
  start-page: 121265
  year: 2022
  ident: ref_9
  article-title: Can customer sentiment impact firm value? An integrated text mining approach
  publication-title: Technol. Forecast. Soc. Chang.
  doi: 10.1016/j.techfore.2021.121265
– volume: 247
  start-page: 108742
  year: 2022
  ident: ref_1
  article-title: Investigating the informativeness of technical indicators and news sentiment in financial market price prediction
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2022.108742
– ident: ref_12
  doi: 10.1145/3520084.3520104
– ident: ref_7
  doi: 10.1109/ICOMET.2019.8673428
– volume: 26
  start-page: 116
  year: 2022
  ident: ref_4
  article-title: A comprehensive review of stock price prediction using text mining
  publication-title: Adv. Decis. Sci.
– ident: ref_36
– ident: ref_3
  doi: 10.3390/bdcc6040130
– ident: ref_19
– volume: 470
  start-page: 443
  year: 2022
  ident: ref_5
  article-title: An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2021.05.103
– ident: ref_22
– volume: 217
  start-page: 119509
  year: 2023
  ident: ref_11
  article-title: News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2023.119509
– volume: 6
  start-page: 93
  year: 2019
  ident: ref_26
  article-title: Smart literature review: A practical topic modelling approach to exploratory literature review
  publication-title: J. Big Data
  doi: 10.1186/s40537-019-0255-7
– volume: 4
  start-page: eaaq1360
  year: 2018
  ident: ref_20
  article-title: A network approach to topic models
  publication-title: Sci. Adv.
  doi: 10.1126/sciadv.aaq1360
– volume: 9
  start-page: 37
  year: 2022
  ident: ref_24
  article-title: Defining user spectra to classify Ethereum users based on their behavior
  publication-title: J. Big Data
  doi: 10.1186/s40537-022-00586-3
– volume: 51
  start-page: 3212
  year: 2019
  ident: ref_6
  article-title: Daily market news sentiment and stock prices
  publication-title: Appl. Econ.
  doi: 10.1080/00036846.2018.1564115
– volume: 3
  start-page: 993
  year: 2003
  ident: ref_18
  article-title: Latent dirichlet allocation
  publication-title: J. Mach. Learn. Res.
– volume: 41
  start-page: 823
  year: 2020
  ident: ref_21
  article-title: Deep LDA: A new way to topic model
  publication-title: J. Inf. Optim. Sci.
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SubjectTerms Analysis
Big data
Comparative studies
Computational linguistics
Computing time
Data mining
Deep learning
Dirichlet problem
Finance
Financial markets
Impact analysis
Language processing
Methods
Modelling
Natural language interfaces
Natural language processing
News
Securities markets
Semantics
Sentiment analysis
Social networks
Subject specialists
Title Leveraging State-of-the-Art Topic Modeling for News Impact Analysis on Financial Markets: A Comparative Study
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