Discovering AI-enabled convergences based on BERT and topic network

Various aspects of artificial intelligence (AI) have become of significant interest to academia and industry in recent times. To satisfy these academic and industrial interests, it is necessary to comprehensively investigate trends in AI-related changes of diverse areas. In this study, we identified and predicted emerging convergences with the help of AI-Associated research s collected from the SCOPUS database. The bidirectional encoder representations obtained via the transformers-based topic discovery technique were subsequently deployed to identify emerging topics related to AI. The topics discovered concern edge computing, biomedical algorithms, predictive defect maintenance, medical applications, fake news detection with block chain, explainable AI and COVID-19 applications. Their convergences were further analyzed based on the shortest path between topics to predict emerging convergences. Our findings indicated emerging AI convergences towards healthcare, manufacturing, legal applications, and marketing. These findings are expected to have policy implications for facilitating the convergences in diverse industries. Potentially, this study could contribute to the exploitation and adoption of AI-enabled convergences from a practical perspective. © 2023 Korean Society for Internet Information. All rights reserved.

Covid-19 and E-Learning: An Exploratory Analysis of Research Topics and Interests in E-Learning During the Pandemic

E-learning has gained further importance and the amount of e-learning research and applications has increased exponentially during the COVID-19 pandemic. Therefore, it is critical to examine trends and interests in e-learning research and applications during the pandemic period. This paper aims to identify trends and research interests in e-learning articles related to COVID-19 pandemic. Consistent with this aim, a semantic content analysis was conducted on 3562 peer-reviewed journal articles published since the beginning of the COVID-19 pandemic, using the N-gram model and Latent Dirichlet Allocation (LDA) topic modeling approach. Findings of the study revealed the high-frequency bigrams such as "online learn ", "online education ", "online teach " and "distance learn ", as well as trigrams such as "higher education institution ", "emergency remote teach ", "education online learn " and "online teach learn ". Moreover, the LDA topic modeling analysis revealed 42 topics. The topics of "Learning Needs ", "Higher Education " and "Social Impact " respectively were the most focused topics. These topics also revealed concepts, dimensions, methods, tools, technologies, applications, measurement and evaluation models, which are the focal points of e-learning field during the pandemic. The findings of the study are expected to provide insights to researchers and future studies.

Topic Discovery About Economy During COVID-19 Pandemic from Spanish Tweets

Automatic topic discovery from natural language texts has been a challenging and widely studied problem. The ability to discover the topics present in a collection of text documents is essential for information systems. Topic discovery has been used to obtain a compact representation of documents for grouping, classification, and retrieval. Some tasks that can benefit from topic discovery: recommendation systems, tracking misinformation, writing summaries, and text clustering. However, topic discovery from Spanish texts has been somewhat neglected. For this reason, this work proposes analyzing the behavior of topic discovery tasks in texts in Spanish, specifically in tweets about the Mexican economy during the COVID-19 pandemic, under three different approaches. A comparison was conducted, achieving promising results because the topic coherence metric indicates coherent topics. The highest score of 1.22 was obtained using PLSA with 50 topics, concluding that the topics encompassed the study domain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter.

Coronavirus disease 2019 (COVID-19) pandemic-related information are flooded on social media, and analyzing this information from an occupational perspective can help us to understand the social implications of this unprecedented disruption. In this study, using a COVID-19-related dataset collected with the Twitter IDs, we conduct topic and sentiment analysis from the perspective of occupation, by leveraging Latent Dirichlet Allocation (LDA) topic modeling and Valence Aware Dictionary and sEntiment Reasoning (VADER) model, respectively. The experimental results indicate that there are significant topic preference differences between Twitter users with different occupations. However, occupation-linked affective differences are only partly demonstrated in our study; Twitter users with different income levels have nothing to do with sentiment expression on covid-19-related topics.