Results 51 to 60 of about 63,655 (233)
n-stage Latent Dirichlet Allocation: A Novel Approach for LDA [PDF]
, 2021 Nowadays, data analysis has become a problem as the amount of data is constantly increasing. In order to overcome this problem in textual data, many models and methods are used in natural language processing. The topic modeling field is one of these methods. Topic modeling allows determining the semantic structure of a text document.
arxiv +1 more source
Evolution of Integrated Marketing Communication Research through Latent Dirichlet Allocation (LDA) Analysis [PDF]
Journal of Emerging Trends in Marketing and Management, 2020 Integrated Marketing Communication (IMC) is an area that emerged as a shift in the way MarCom departments were functioning at the beginning of 90's. For the last 30 years, the concept evolved from being a tactical set of actions to a customer-focused ...
Alina Popa, Raluca-Ecaterina Brandabur
doaj
Extending Information Retrieval Methods to Personalized Genomic-Based Studies of Disease
Cancer Informatics, 2014 Genomic-based studies of disease now involve diverse types of data collected on large groups of patients. A major challenge facing statistical scientists is how best to combine the data, extract important features, and comprehensively characterize the ...
Shuyun Ye +2 more
doaj +1 more source
User–Topic Modeling for Online Community Analysis
Applied Sciences, 2020 Analyzing user behavior in online spaces is an important task. This paper is dedicated to analyzing the online community in terms of topics. We present a user–topic model based on the latent Dirichlet allocation (LDA), as an application of topic modeling
Sung-Hwan Kim, Hwan-Gue Cho
doaj +1 more source
Topic model for graph mining based on hierarchical Dirichlet process
Statistical Theory and Related Fields, 2020 In this paper, a nonparametric Bayesian graph topic model (GTM) based on hierarchical Dirichlet process (HDP) is proposed. The HDP makes the number of topics selected flexibly, which breaks the limitation that the number of topics need to be given in ...
Haibin Zhang, Shang Huating, Xianyi Wu
doaj +1 more source
International Journal for Innovation Education and Research, 2020 The aim of this paper is provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modelling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a
openaire +2 more sources
Regional Shopping Objectives in British Grocery Retail Transactions Using Segmented Topic Models
Applied Stochastic Models in Business and Industry, EarlyView.ABSTRACT Understanding the customer behaviours behind transactional data has high commercial value in the grocery retail industry. Customers generate millions of transactions every day, choosing and buying products to satisfy specific shopping needs.
Mariflor Vega Carrasco +4 more
wiley +1 more source
Ingeniería Investigación y Desarrollo, 2021 En esta investigación, se evaluaron ensayos sobre la preservación de árboles de estudiantes de cuarto grado (escuela primaria de Colombia) con Latent Dirichlet Allocation (LDA).
Camilo Arturo Suárez Ballesteros +2 more
doaj +1 more source
Algorithmic management in the gig economy: A systematic review and research integration
Journal of Organizational Behavior, EarlyView.Summary Rapid growth in the gig economy has been facilitated by the increased use of algorithmic management (AM) in online platforms (OPs) coordinating gig work. There has been a concomitant increase in scholarship related to AM across scientific domains (e.g., computer science, engineering, operations management, management, sociology, and law ...
Imran Kadolkar +2 more
wiley +1 more source
Latent Dirichlet Allocation Model Training with Differential Privacy [PDF]
arXiv, 2020 Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various applications. However, the LDA model as well as the training process of LDA may expose the text information in the training data, thus bringing significant privacy concerns.
arxiv