Towards Predicting Trend of Scientific Research Topics using Topic Modeling

Abuhay, Tesfamariam M.; Nigatie, Yemisrach G.; Kovalchuk, Sergey V.

doi:10.1016/j.procs.2018.08.284

Cited by 21 publications

(21 citation statements)

References 12 publications

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“…A few studies have been carried on predicting the trend of the future research topic. For example, the traditional time series prediction method ARIMA [ 12 ] has been employed to predict the development trend of research topics of conference papers on computer science discipline, which contains a total of 5982 papers over 17 years. Saman et al construct a scientific knowledge network by using the keywords of articles in computer science journals and conferences and use the link prediction method to predict the future structure of the keyword networks [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

Guan

et al. 2021

Computational Intelligence and Neuroscience

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Computer science discipline includes many research fields, which mutually influence and promote each other’s development. This poses two great challenges of predicting the research topics of each research field. One is how to model fine-grained topic representation of a research field. The other is how to model research topic of different fields and keep the semantic consistency of research topics when learning the scientific influence context from other related fields. Unfortunately, the existing research topic prediction approaches cannot handle these two challenges. To solve these problems, we employ multiple different Recurrent Neural Network chains which model research topics of different fields and propose a research topic prediction model based on spatial attention and semantic consistency-based scientific influence modeling. Spatial attention is employed in field topic representation which can selectively extract the attributes from the field topics to distinguish the importance of field topic attributes. Semantic consistency-based scientific influence modeling maps research topics of different fields to a unified semantic space to obtain the scientific influence context of other related fields. Extensive experiment results on five related research fields in the computer science (CS) discipline show that the proposed model is superior to the most advanced methods and achieves good topic prediction performance.

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Section: Introductionmentioning

confidence: 99%

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

Guan

et al. 2021

Computational Intelligence and Neuroscience

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“…Using topic modeling for getting the trend of technological research topics is the present, and will be the future state, of these research areas. Abuhay (2018) [7] proposed a hot topic life cycle model (HTLCM) to find out the evaluation trend of a topic and Du et al (2020) [8] proposed an algorithm by integrating HTLCM with a micro-blog features latent Dirichlet allocation (MF-LDA) model. The technology affects the system level tradeoffs that shape the overall system design (Paul and Thomas, 2012) [9].…”

Section: Topic Modelingmentioning

confidence: 99%

Analysis of Technological Trends and Technological Portfolio of Unmanned Aerial Vehicle

Battsengel

Geetha

Jeon

2020

Journal of Open Innovation: Technology, Market, and Complexity

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Unmanned aerial vehicles (UAVs) are predicted to be the most dynamic growth sector of the world aerospace market this decade. This technology is swiftly strengthening its presence in multiple fields of human life. In the global aerospace industry, UAVs have become an essential means of securing growth and competitiveness. The investment in UAVs is estimated at billions of dollars in developed countries. However, despite the rapid growth of UAVs, few studies have classified UAV technologies into detailed technologies, or analyzed the technology development trends of UAVs. We used the latent Dirichlet allocation (LDA) method of topic modeling and network analysis to analyze the technological trends and technological portfolio of UAVs. The UAV patents were collected from the Korea Intellectual Property Rights Information Service. Analyzing the results shows that the most popular topics are communication technology (34.1%), power supply (19.5%) and navigation system (17.6%). The technological portfolio of a UAV is constituted of three groups: control system, operation system and device. This study is expected to be guidelines for developers and policy-makers on UAVs.

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“…With the development of computer science, few studies have also begun to focus on the topic evolution trend forecasting of computer science. Abuhay et al employ the classic time series forecasting model AutoRegressive Integrated Moving Averages (ARIMAs) to predict the trend of research topics of international conference of computer science 17 . With the development of deep neural networks, 18 long short‐term memory (LSTM) is employed to capture timing sequence features, 19 and used in topic prediction tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Abuhay et al employ the classic time series forecasting model AutoRegressive Integrated Moving Averages (ARIMAs) to predict the trend of research topics of international conference of computer science. 17 With the development of deep neural networks, 18 long short-term memory (LSTM) is employed to capture timing sequence features, 19 and used in topic prediction tasks. Using 19,164 publications and 25 journals, Liang et al first predict the future popularity scores of candidate topics in a time series, and then apply LSTM to predict the popularity scores of candidate topics to determine future research topics.…”

Section: Introductionmentioning

confidence: 99%

A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network

Xue

et al. 2022

Int J of Intelligent Sys

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In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Accurately and effectively predicting the trends of future research topics can help researchers discover future research hotspots.However, due to the increasingly close correlation between various research themes, there is a certain dependency relationship between a large number of research themes. Viewing a single research theme in isolation and using traditional sequence problem processing methods cannot effectively explore the spatial dependencies between these research themes. To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional network model. Our model combines a graph convolutional neural network (GCN) and Temporal Convolutional Network (TCN), specifically, GCNs are used to learn the spatial dependencies of research topics a and use space dependence to strengthen spatial characteristics. TCN is used to learn the dynamics of research topics' trends. Optimization is based on the calculation of weighted losses based on time distance. Compared with the current mainstream sequence prediction models and similar spatiotemporal models on the paper datasets, experiments show that, in research topic prediction tasks, our model can effectively capture spatiotemporal relationships and the predictions outperform state-of-art baselines.

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Towards Predicting Trend of Scientific Research Topics using Topic Modeling

Cited by 21 publications

References 12 publications

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

Analysis of Technological Trends and Technological Portfolio of Unmanned Aerial Vehicle

A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network

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