Discovering topics and trends in the field of Artificial Intelligence: Using LDA topic modeling

Yu, Dejian; Xiang, Bo

doi:10.1016/j.eswa.2023.120114

Cited by 19 publications

(4 citation statements)

References 55 publications

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“…However, these models are not perfect, which means that they deserve to be further improved. According to the literature, the algorithms used in these models are mainly machine learning, deep learning and convolutional neural networks, and machine learning is currently one of the most mentioned techniques in the field of AI ( 61 ). Therefore, we recommend that researchers pay more attention to these techniques and improve the old or develop new models based on them.…”

Section: Discussionmentioning

confidence: 99%

“…An article focused on the hotspots and trends in the field of AI revealed that robots were a highly concerned topic in this field, including human-robot interaction, robot manipulation, robot grasp design, etc. ( 61 ) This suggests that AI has a broad application prospect in surgical robots, and researchers can make new explorations based on the above directions. Published research on AI and surgical robots is also worth referencing, including directions such as autonomous surgical robots, motion analysis, and presurgical planning ( 15 , 67 ).…”

Section: Discussionmentioning

confidence: 99%

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Global research trends of the application of artificial intelligence in bladder cancer since the 21st century: a bibliometric analysis

Zhou,

Xu,

Zeng

et al. 2023

Front. Oncol.

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IntroductionSince the significant breakthroughs in artificial intelligence (AI) algorithms, the application of AI in bladder cancer has rapidly expanded. AI can be used in all aspects of the bladder cancer field, including diagnosis, treatment and prognosis prediction. Nowadays, these technologies have an excellent medical auxiliary effect and are in explosive development, which has aroused the intense interest of researchers. This study will provide an in-depth analysis using bibliometric analysis to explore the trends in this field.MethodDocuments regarding the application of AI in bladder cancer from 2000 to 2022 were searched and extracted from the Web of Science Core Collection. These publications were analyzed by bibliometric analysis software (CiteSpace, Vosviewer) to visualize the relationship between countries/regions, institutions, journals, authors, references, keywords.ResultsWe analyzed a total of 2368 publications. Since 2016, the number of publications in the field of AI in bladder cancer has increased rapidly and reached a breathtaking annual growth rate of 43.98% in 2019. The U.S. has the largest research scale, the highest study level and the most significant financial support. The University of North Carolina is the institution with the highest level of research. EUROPEAN UROLOGY is the most influential journal with an impact factor of 24.267 and a total citation of 11,848. Wiklund P. has the highest number of publications, and Menon M. has the highest number of total citations. We also find hot research topics within the area through references and keywords analysis, which include two main parts: AI models for the diagnosis and prediction of bladder cancer and novel robotic-assisted surgery for bladder cancer radicalization and urinary diversion.ConclusionAI application in bladder cancer is widely studied worldwide and has shown an explosive growth trend since the 21st century. AI-based diagnostic and predictive models will be the next protagonists in this field. Meanwhile, the robot-assisted surgery is still a hot topic and it is worth exploring the application of AI in it. The advancement and application of algorithms will be a massive driving force in this field.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Global research trends of the application of artificial intelligence in bladder cancer since the 21st century: a bibliometric analysis

Zhou,

Xu,

Zeng

et al. 2023

Front. Oncol.

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“…P ( w i | z p ) is the probability that the word w i occurs in z p . It is clear that perplexity is mainly influenced by M , N and K according to equation [ 69 ]. By comparing the perplexity scores for different numbers of topics, the optimal number of LDA topics to model can be determined to obtain the best fit on a given dataset, while also being mindful of overfitting issues [ 70 ].…”

Section: Methodsmentioning

confidence: 99%

The public attitude towards ChatGPT on reddit: A study based on unsupervised learning from sentiment analysis and topic modeling

Xu,

Fang,

Huang

et al. 2024

PLoS ONE

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ChatGPT has demonstrated impressive abilities and impacted various aspects of human society since its creation, gaining widespread attention from different social spheres. This study aims to comprehensively assess public perception of ChatGPT on Reddit. The dataset was collected via Reddit, a social media platform, and includes 23,733 posts and comments related to ChatGPT. Firstly, to examine public attitudes, this study conducts content analysis utilizing topic modeling with the Latent Dirichlet Allocation (LDA) algorithm to extract pertinent topics. Furthermore, sentiment analysis categorizes user posts and comments as positive, negative, or neutral using Textblob and Vader in natural language processing. The result of topic modeling shows that seven topics regarding ChatGPT are identified, which can be grouped into three themes: user perception, technical methods, and impacts on society. Results from the sentiment analysis show that 61.6% of the posts and comments hold favorable opinions on ChatGPT. They emphasize ChatGPT’s ability to prompt and engage in natural conversations with users, without relying on complex natural language processing. It provides suggestions for ChatGPT developers to enhance its usability design and functionality. Meanwhile, stakeholders, including users, should comprehend the advantages and disadvantages of ChatGPT in human society to promote ethical and regulated implementation of the system.

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“…These embeddings served as vector portrayals of individual documents within a high-dimensional space, with the advantage of encapsulating semantic resemblances among the documents. Unlike a traditional Latent Dirichlet Allocation (LDA) approach (Yu & Xiang, 2023), the topic modeling algorithm employed for the analysis in this article is notable for its absence of assumptions regarding the intrinsic quantity of underlying topics.…”

Section: Data Analytic Methodsmentioning

confidence: 99%

Enhancing dental and medical students’ self-regulated learning through multiple choice questions: An evaluative study using machine learning

Leth Rasmussen,

Have Musaeus,

Dahl

et al. 2024

LOM

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This article reports a mixed methods study that uses machine learning and thematic analysis to investigate student evaluation of multiple-choice questions (MCQ). The focus is on medical and dental students' experience of self-regulated learning and motivation. We evaluate two systems developed at Aarhus University: "MED MCQ" used by medical students and "MCQ anatomy" used by dental students. We evaluate through two surveys in SurveyXact with responses from 126 medical students and 70 dental students. We use topic modelling over free text responses. The machine-learning model identifies two groups of students who, in different ways, both experience the system as motivating and facilitating their learning process. The students experience increased self-regulated learning by being able to choose the form of presentation of questions and answer questions independently of the instructor. The article discusses how educators and developers can use MCQs to promote student learning and how to analyze open-ended questions. We discuss the potential for using machine learning and integrating MCQ systems into teaching.

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Discovering topics and trends in the field of Artificial Intelligence: Using LDA topic modeling

Cited by 19 publications

References 55 publications

Global research trends of the application of artificial intelligence in bladder cancer since the 21st century: a bibliometric analysis

Global research trends of the application of artificial intelligence in bladder cancer since the 21st century: a bibliometric analysis

The public attitude towards ChatGPT on reddit: A study based on unsupervised learning from sentiment analysis and topic modeling

Enhancing dental and medical students’ self-regulated learning through multiple choice questions: An evaluative study using machine learning

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