IMPACT OF N-STAGE LATENT DIRICHLET ALLOCATION ON ANALYSIS OF HEADLINE CLASSIFICATION

Küçük Resim Yok

Tarih

2022

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Agh Univ Science & Technology Press

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

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 over-come this problem in text analyses for downstream tasks (such as sentiment analysis, spam detection, and news classification). In this research, we bench-mark 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 classi-fication. We ran our experiments on three and five classes of publicly available Turkish and English datasets. We have demonstrated that, as a feature ex-tractor, 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.

Açıklama

Anahtar Kelimeler

Topic Modeling, Headline Classification, Machine Learning, Text Classification, Latent Dirichlet Allocation, Data Analysis

Kaynak

Computer Science-Agh

WoS Q Değeri

N/A

Scopus Q Değeri

Q4

Cilt

23

Sayı

3

Künye