Algorithms for Topic Modeling

Dear Colleagues,

The idea of using non-negative matrix factorization (NMF) for topic modeling was first introduced by Lee and Seung (1999) and popularized by Blei et al. (2003) with the publication of the latent Dirichlet allocation (LDA) algorithm. Both NMF and LDA are able to discover latent topics within a document collection, but take different mathematical approaches. NMF produces a matrix decomposition where the resulting matrices contain positive values and map topics to documents and words. LDA is a probabilistic graphical model that represents a topic as a mixture of documents and words. There are many different algorithmic approaches to topic modeling, and researchers continue to seek advances in algorithm design, implementation and evaluation.

The field of topic modeling is constantly evolving, and there are opportunities for new research directions. Recently, there has been interest in using deep neural networks for topic modeling. These approaches have shown promise for improved topic coherence, but have scalability and deployment issues due to computational requirements. Algorithms (ISSN 1999-4893; CODEN: ALGOCH) is a leading open-access journal and seeks high-quality journal articles that explore recent advances in algorithms and deep neural approaches to topic modeling.

Researchers are invited to submit original papers on all aspects of topic modeling, including but not limited to:

• Novel variants of LDA, NMF and neural-inspired algorithms for topic modeling.
• Analysis and comparison of existing topic modeling algorithms.
• Evaluation metrics for topic modeling.
• Deep NMF architectures for topic modeling.
• Improving the scalability of neural-inspired algorithms for topic modeling.
• Algorithms to support cross-lingual topic modeling.
• Topic modeling algorithms that allow the inclusion of domain information (e.g., vectors obtained from large language models such as BERT or transformer models).
• The use of topic modeling algorithms to assist with the interpretation of large language models (using transformer architectures) and deep neural networks.
• Applications of topic modeling algorithms within varied domain areas including but not limited to natural language processing, bioinformatics and computer vision.
• Smart user interfaces for user interaction and steering of topic modeling algorithm output.

Usability studies on domain expert interpretation and judgment of topic modeling output.

Dr. Aneesha Bakharia
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Researchers are invited to submit original papers on all aspects of topic modeling, including but not limited to:

• Novel variants of LDA, NMF and neural-inspired algorithms for topic modeling.
• Analysis and comparison of existing topic modeling algorithms.
• Evaluation metrics for topic modeling.
• Deep NMF architectures for topic modeling.
• Improving the scalability of neural-inspired algorithms for topic modeling.
• Algorithms to support cross-lingual topic modeling.
• Topic modeling algorithms that allow the inclusion of domain information (e.g., vectors obtained from large language models such as BERT or transformer models).
• The use of topic modeling algorithms to assist with the interpretation of large language models (using transformer architectures) and deep neural networks.
• Applications of topic modeling algorithms within varied domain areas including but not limited to natural language processing, bioinformatics and computer vision.
• Smart user interfaces for user interaction and steering of topic modeling algorithm output.