Topic prediction and knowledge discovery based on integrated topic modeling and deep neural networks approaches

Abstract

Understanding the real-world short texts become an essential task in the recent research area. The document deduction analysis and latent coherent topic named as the important aspect of this process. Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are suggested to model huge information and documents. This type of contexts’ main problem is the information limitation, words relationship, sparsity, and knowledge extraction. The knowledge discovery and machine learning techniques integrated with topic modeling were proposed to overcome this issue. The knowledge discovery was applied based on the hidden information extraction to increase the suitable dataset for further analysis. The integration of machine learning techniques, Artificial Neural Network (ANN) and Long Short-Term (LSTM) are applied to anticipate topic movements. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. We demonstrate general information from different techniques applied in short text topic modeling. We proposed three categories based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation using representative design and analysis of all categories’ performance in different tasks. Finally, the proposed system evaluates with state-of-art methods on real-world datasets, comprises them with long document topic modeling algorithms, and creates a classification framework that considers further knowledge and represents it in the machine learning pipeline.

[Retracted] Construction and Simulation of Financial Audit Model Based on Convolutional Neural Network

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Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

“…It combines the extended NMF and clustering mechanism by presenting topic regularization and document regularization, respectively, to mitigate the data sparsity issue in the short text. Shahbazi and Byun (2021) proposed a model to anticipate the topics and knowledge discovery that integrates deep learning such as Artificial Neural Network (ANN) and LSTM with topic modelling and machine learning. This model overcomes data sparsity, data limitation, and word relationship issues.…”

NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security

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