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

Guven Zekeriya Anil, Diri Banu, Cakaloglu Tolgahan

Computer Science

2022

T. 23 (3)

375--394

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.

Text-mining-based approach for conductingliterature review of selected meshfree methods

Sindhusuta S., Chi Sheng-Wei, Derrible Sybil

Computer Assisted Methods in Engineering and Science

2021

Vol. 28, no. 4

265--290

The goal of this study is to review the literature in the field ofmeshfree methodsusing textmining. For this study, the abstracts of around 17330 relevant articles published from 1990to 2020 were collected from Scopus. Text mining techniques such as the latent Dirichletallocation (LDA), along with the calculation of term frequencies and co-occurrence coefficients were used to analyze the text. The study identified a few key topics in the field ofmeshfree methods and helped to see the evolution of the field over the past three decades.Furthermore, the trend in the number of publications and frequency map highlightedresearch trends and lack of focus in certain areas. The co-author network visualizationprovided interesting insights about collaboration between different researchers around theworld. Overall, this study facilitates a systematic literature review in the field of meshfreemethods and provides a broader perspective of the field to the research community.