Impact of n-stage latent Dirichlet allocation on analysis of headline classification

• Autorzy: Guven Zekeriya Anil , Diri Banu , Cakaloglu Tolgahan

Identyfikatory

DOI

10.7494/csci.2022.23.3.4622

• Języki publikacji: EN

Abstrakty

EN

Data analysis becomes difficult when the amount of the data increases. More specifically, extracting meaningful insights from this vast amount of data and grouping it based on its shared features without human intervention requires advanced methodologies. There are topic-modeling methods that help overcome this problem in text analyses for downstream tasks (such as sentiment analysis, spam detection, and news classification). In this research, we benchmark several classifiers (namely, random forest, AdaBoost, naive Bayes, and logistic regression) using the classical latent Dirichlet allocation (LDA) and n-stage LDA topic-modeling methods for feature extraction in headline classification. We ran our experiments on three and five classes of publicly available Turkish and English datasets. We have demonstrated that, as a feature extractor, n-stage LDA obtains state-of-the-art performance for any downstream classifier. It should also be noted that random forest was the most successful algorithm for both datasets.

Słowa kluczowe

EN

topic modeling; headline classification; machine learning; text classification; latent Dirichlet allocation; data analysis

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2022
• Tom: T. 23 (3)
• Strony: 375--394
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Guven Zekeriya Anil

• Ege University, Turkey

autor

Diri Banu

• Yildiz Technical University, Turkey

autor

Cakaloglu Tolgahan

• Walmart Global Tech, USA

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).