Topic Modeling Using Latent Dirichlet allocation

We are not able to deal with a mammoth text corpus without summarizing them into a relatively small subset. A computational tool is extremely needed to understand such a gigantic pool of text. Probabilistic Topic Modeling discovers and explains the enormous collection of documents by reducing them in a topical subspace. In this work, we study the background and advancement of topic modeling techniques. We first introduce the preliminaries of the topic modeling techniques and review its extensions and variations, such as topic modeling over various domains, hierarchical topic modeling, word embedded topic models, and topic models in multilingual perspectives. Besides, the research work for topic modeling in a distributed environment, topic visualization approaches also have been explored. We also covered the implementation and evaluation techniques for topic models in brief. Comparison matrices have been shown over the experimental results of the various categories of topic modeling. Diverse technical challenges and future directions have been discussed.

Graph Neural Collaborative Topic Model for Citation Recommendation

Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations. However, existing RTMs can only capture pairwise or direct (first-order) citation relationships among documents. The indirect (high-order) citation links have been explored in graph neural network–based methods, but these methods suffer from the well-known explainability problem. In this article, we propose a model called Graph Neural Collaborative Topic Model that takes advantage of both relational topic models and graph neural networks to capture high-order citation relationships and to have higher explainability due to the latent topic semantic structure. Experiments on three real-world citation datasets show that our model outperforms several competitive baseline methods on citation recommendation. In addition, we show that our approach can learn better topics than the existing approaches. The recommendation results can be well explained by the underlying topics.

The Evolution of Topic Modeling

Topic models have been applied to everything from books to newspapers to social media posts in an effort to identify the most prevalent themes of a text corpus. We provide an in-depth analysis of unsupervised topic models from their inception to today. We trace the origins of different types of contemporary topic models, beginning in the 1990s, and we compare their proposed algorithms, as well as their different evaluation approaches. Throughout, we also describe settings in which topic models have worked well and areas where new research is needed, setting the stage for the next generation of topic models.

Emergence and Rapid Popularization of Paid Web-Conferencing-Application-Based Tours in Japan: An Analysis of Their Business Potential

Following the emergence of COVID-19 pandemic, people in Japan were asked to refrain from traveling, resulting in various companies coming up with new ways of experiencing tourism. Among them, the online tourism experience of H.I.S. Co., Ltd. (HIS) drew more than 100,000 participants as of August 29, 2021. In this study, we focused on an online tour where the host goes to the site and records real time communication using a web conference application. The destinations of online tours were analyzed through text mining, and the characteristics of online tours were analyzed using Latent Dirichlet Allocation (LDA) of topic models. The results show that the number of online tours is weakly negatively correlated with distance and time differences. From the topic model, it is evident that the guide is important in online tours. In addition, the sense of presence, communication environment, and images, which are considered to be unique topics in online tours, are also relevant to the evaluation.

EXTRACTING TOPICS FROM A TV CHANNEL'S FACEBOOK PAGE USING CONTEXTUALIZED DOCUMENT EMBEDDING

Topic models extract meaningful words from text collection, allowing for a better understanding of data. However, the results are often not coherent enough, and thus harder to interpret. Adding more contextual knowledge to the model can enhance coherence. In recent years, neural network-based topic models become available, and the development level of the neural model has developed thanks to BERT-based representation. In this study, we suggest a model extract news on the Aljazeera Facebook page. Our approach combines the neural model (ProdLDA) and the Arabic Pre-training BERT transformer model (AraBERT). Therefore, the proposed model produces more expressive and consistent topics than ELMO using different topic model algorithms (ProdLDA and LDA) with 0.883 in topic coherence.

Exploring the digital humanities research agenda: a text mining approach

PurposeThis study aims to explore knowledge structure and research trends in the domain of digital humanities (DH) in the recent decade. The study identified prevailing topics and then, analyzed trends of such topics over time in the DH field.Design/methodology/approachResearch bibliographic data in the area of DH were collected from scholarly databases. Multiple text mining techniques were used to identify prevailing research topics and trends, such as keyword co-occurrences, bigram analysis, structural topic models and bi-term topic models.FindingsTerm-level analysis revealed that cultural heritage, geographic information, semantic web, linked data and digital media were among the most popular topics in the recent decade. Structural topic models identified that linked open data, text mining, semantic web and ontology, text digitization and social network analysis received increased attention in the DH field.Originality/valueThis study applied existent text mining techniques to understand the research domain in DH. The study collected a large set of bibliographic text, representing the area of DH from multiple academic databases and explored research trends based on structural topic models.