Mapping and predicting research trends in international journal publications using graph and topic modeling

Suyanto, Asep Herman; Djatna, Taufik; Wijaya, Sony Hartono

doi:10.11591/ijeecs.v30.i2.pp1201-1213

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…The use of RMSE is quite common and considered an excellent general error metric for numerical predictions. The smaller the RMSE value, the closer the predicted and observed values are [25]. Therefore, if the model's objective is to predict daily stock prices (time series problems), it will be assessed using the RMSE approach.…”

Section: Discussionmentioning

confidence: 99%

Associating deep learning and the news headlines sentiment for Bursa stock price prediction

Sidek

Ahmad

Teo

2023

IJEECS

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Accurate stock price prediction is appealing to academics, economists, and financial analysts for its potential to increase profits. Although remarkable progress has been made in stock prediction accuracy, studies to explore the relationship between public sentiments and the prediction of stock price movement based on online news portals in Malaysia context are limited. Therefore, this study aims to determine whether news sentiments influence the movement of the Bursa stock price. The stock prediction model was implemented using long short-term memory (LSTM), with stock data from Bursa Malaysia between January 2017 and April 2022, and the root mean squared error (RMSE) value was calculated. In addition, LSTM prediction model was compared to the decision tree algorithm, and LSTM performed significantly better than the decision tree, particularly when using the New York stock exchange (NYSE) dataset. Furthermore, sentiment analysis was carried out using a Malaysian online news portal's business and financial news. The findings showed that i) news has a significant impact on Malaysian stock market price movement; ii) the RMSE of the LSTM model was improved by adding a parameter (news polarity values); and iii) the RMSE value generated is less than one for every company stock and is influenced by stock price and price change magnitude.

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Section: Discussionmentioning

confidence: 99%

Associating deep learning and the news headlines sentiment for Bursa stock price prediction

Sidek

Ahmad

Teo

2023

IJEECS

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“…Identifying the topics of scientific articles is instrumental in enabling researchers to track research trends and identify emerging areas of interest within their field [4], [5], [6], [7]. Moreover, it allows researchers to contextualize their own work within the broader landscape of their discipline and highlight how their work addresses critical questions or contributes to existing knowledge gaps [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Topic Modeling for Scientific Articles: Exploring Optimal Hyperparameter Tuning in BERT

Wijanto,

Widiastuti,

Yong

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Topic modeling has emerged as a successful approach to uncovering topics from textual data. Various topic modeling techniques have been introduced, ranging from traditional algorithms to those based on neural networks. In this research, we explore advanced topic modeling techniques, including BERT-based approaches, to enhance the analysis of scientific articles. We first investigate a widely used Latent Dirichlet Allocation (LDA) model and then explore the capabilities of BERT, to automatically uncover latent topics within scientific papers. The goal of this study is to identify the optimal hyperparameter setting for BERT-based topic modeling of scientific articles. We conduct experiments across several scenarios involving combinations of word embedding, dimension reduction, and clustering methods. The results were analyzed based on the coherence values, average execution time, number of topics generated, visualization through the inter-topic distance map, and the top-N-words of each topic. Our findings suggest that combination of RoBERTa for word embedding, PCA for dimension reduction, and K-Means for clustering yields superior results among the tested scenarios. Further evaluation of BERT-based topic modeling is necessary to validate these findings and explore its applications in various academic and industrial contexts. The implications of these advanced techniques could significantly streamline the process of staying updated with scientific literature, potentially revolutionizing research methodologies across disciplines.

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“…Because of the high number of scientific publications and their rapid expansion, the amount of research is now immense, and its content is so complicated that traditional ways of interpreting the smart city, such as literature reviews, content analysis, and case studies [13], may not be adequate to conduct quantitative analyses, analyze patterns, or make judgments without the assistance of computers. With so many scientific papers available, computer-assisted data summarization is essential [14].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis

Kousis,

Tjortjis

2023

Future Internet

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In recent years, the emergence of the smart city concept has garnered attention as a promising innovation aimed at addressing the multifactorial challenges arising from the concurrent trends of urban population growth and the climate crisis. In this study, we delve into the multifaceted dimensions of the smart city paradigm to unveil its underlying structure, employing a combination of quantitative and qualitative techniques. To achieve this, we collected textual data from three sources: scientific publication abstracts, news blog posts, and social media entries. For the analysis of this textual data, we introduce an innovative semi-automated methodology that integrates topic modeling and thematic analysis. Our findings highlight the intricate nature of the smart city domain, which necessitates examination from three perspectives: applications, technology, and socio-economic perspective. Through our analysis, we identified ten distinct aspects of the smart city paradigm, encompassing mobility, energy, infrastructure, environment, IoT, data, business, planning and administration, security, and people. When comparing the outcomes across the three diverse datasets, we noted a relative lack of attention within the scientific community towards certain aspects, notably in the realm of business, as well as themes relevant to citizens’ everyday lives, such as food, shopping, and green spaces. This work reveals the underlying thematic structure of the smart city concept to help researchers, practitioners, and public administrators participate effectively in smart city transformation initiatives. Furthermore, it introduces a novel data-driven method for conducting thematic analysis on large text datasets.

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Mapping and predicting research trends in international journal publications using graph and topic modeling

Cited by 3 publications

References 36 publications

Associating deep learning and the news headlines sentiment for Bursa stock price prediction

Associating deep learning and the news headlines sentiment for Bursa stock price prediction

Topic Modeling for Scientific Articles: Exploring Optimal Hyperparameter Tuning in BERT

Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis

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