Dynamic Topic-Noise Models for Social Media

Churchill, Rob; Singh, Lisa

doi:10.1007/978-3-031-05936-0_34

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…A similar approach was proposed to discover the evolution of artificial intelligence in cancer studies [20]. Implementation of DTM was also done on the social media platforms [21].…”

Section: Implementation Of Dynamic Topic Modeling To Discover Topic E...mentioning

confidence: 99%

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Hananto

2023

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Annotation and analysis of online customer reviews were identified as significant problems in various domains, including business intelligence, marketing, and e-governance. In the last decade, various approaches based on topic modeling have been developed to solve this problem. The known solutions, however, often only work well on content with static topics. As a result, it is challenging to analyze customer reviews that include dynamic and constantly expanding collections of short and noisy texts. A method was proposed to handle such dynamic content. The proposed system applied a dynamic topic model using BERTopic to monitor topics and word evolution over time. It would help decide when the topic model needs to be retrained to capture emerging topics. Several experiments were conducted to test the practicality and effectiveness of the proposed framework. It demonstrated how a dynamic topic model could handle the emergence of new and over-time-correlated topics in customer review data. As a result, improved performance was achieved compared to the baseline static topic model, with 25% of new segmented texts discovered using the dynamic topic model. Experimental results have, therefore, convincingly demonstrated that the proposed framework can be used in practice to develop automatic review annotation tools.

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“…A similar approach was proposed to discover the evolution of artificial intelligence in cancer studies [20]. Implementation of DTM was also done on the social media platforms [21].…”

Section: Implementation Of Dynamic Topic Modeling To Discover Topic E...mentioning

confidence: 99%

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Hananto

2023

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Enhancing emerging technology discovery in nanomedicine by integrating innovative sentences using BERT and NLDA

Wang,

Liu,

Zhu

2024

Journal of Data and Information Science

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Purpose Nanomedicine has significant potential to revolutionize biomedicine and healthcare through innovations in diagnostics, therapeutics, and regenerative medicine. This study aims to develop a novel framework that integrates advanced natural language processing, noise-free topic modeling, and multidimensional bibliometrics to systematically identify emerging nanomedicine technology topics from scientific literature. Design/methodology/approach The framework involves collecting full-text articles from PubMed Central and nanomedicine-related metrics from the Web of Science for the period 2013–2023. A fine-tuned BERT model is employed to extract key informative sentences. Noiseless Latent Dirichlet Allocation (NLDA) is applied to model interpretable topics from the cleaned corpus. Additionally, we develop and apply metrics for novelty, innovation, growth, impact, and intensity to quantify the emergence of novel technological topics. Findings By applying this methodology to nanomedical publications, we identify an increasing emphasis on research aligned with global health priorities, particularly inflammation and biomaterial interactions in disease research. This methodology provides deeper insights through full-text analysis and leading to a more robust discovery of emerging technologies. Research limitations One limitation of this study is its reliance on the existing scientific literature, which may introduce publication biases and language constraints. Additionally, manual annotation of the dataset, while thorough, is subject to subjectivity and can be time-consuming. Future research could address these limitations by incorporating more diverse data sources, and automating the annotation process. Practical implications The methodology presented can be adapted to explore emerging technologies in other scientific domains. It allows for tailored assessment criteria based on specific contexts and objectives, enabling more precise analysis and decision-making in various fields. Originality/value This study offers a comprehensive framework for identifying emerging technologies in nanomedicine, combining theoretical insights and practical applications. Its potential for adaptation across scientific disciplines enhances its value for future research and decision-making in technology discovery.

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Dynamic Topic-Noise Models for Social Media

Cited by 2 publications

References 16 publications

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Enhancing emerging technology discovery in nanomedicine by integrating innovative sentences using BERT and NLDA

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