Building a TIN-LDA Model for Mining Microblog Users’ Interest

Zheng, Wei; Ge, Baozhen; Wang, Chishe

doi:10.1109/access.2019.2897910

Cited by 20 publications

(5 citation statements)

References 27 publications

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“…Qualitative analysis The qualitative analysis highlighted some observations related to existing TSTTM and ASTTM models. According to the existing TSTTM models, it can be observed that from Tables 2, 3, and 4, some of the works tried to increase the accuracy, such as (Wang et al 2012;Fang et al 2017;Kumar and Vardhan 2019;Sharath et al 2019;Valdez et al 2018;Belford et al 2016;Yan et al 2012;Muliawati and Murfi 2017;Capdevila et al 2017;and Li et al 2015), enhance the coherence such as (Zhao et al 2011;Zheng et al 2019;Han et al 2020;Kim et al 2020;Farahat et al 2015;Lacoste-Julien et al 2009;and He et al 2019b), alleviate the data sparsity problem as in (Ozyurt and Akcayol 2021;Akhtar et al 2019b;Iskandar 2017;and Pang et al 2019), extract the topics from the unlabeled data (Korshunova et al 2019;Ozyurt and Akcayol 2021). Other existing works resolved the lack of semantic information and local word co-occurrence (Akhtar et al 2019a;Chen and Kao 2017;Chen et al 2020b).…”

Section: Discussionmentioning

confidence: 99%

“…Tajbakhsh and Bagherzadeh (2019) designed the Semantic knowledge LDA with topic vector extracting tweet topics based on the co-occurrence of word for recommendation system. Zheng et al (2019) developed the Three-layer Interest Network LDA (TIN-LDA) model to discover topics from tweet data through interest attributes. The TIN-LDA significantly extracts the semantic correlation among the keywords and thus enhancing the coherence of the topics.…”

Section: Probabilistic Based Modelsmentioning

confidence: 99%

“…Collected from Weibo CHI (Ni et al 2018;Yuan Zuo, Zhao, et al 2016b;Liqing et al 2019;Li et al 2013;Zheng et al 2019) Specifically TweetSet EN (Yin and Wang 2014;Wu and Li 2019;Qiang et al 2018b;and Mazarura et al 2020) Publically Tweets EN (Jiang et al 2018;Wang et al 2016;Zuo, Wu, et al 2016a ;Lin et al 2020a;Zuo et al 2021;Chen et al 2020b;and Liqing et al 2019) Publically Tweets2011 EN (Cheng et al 2014;Liang et al 2018;Li et al 2019b;Sharath et al 2019;Rashid et al 2019b;Yan et al 201...…”

Section: Specificallymentioning

confidence: 99%

“…Twitter DataSet EN (Nguyen et al 2015;and Yi et al 2020) Publically Sina Weibo CHI (Yu and Qiu 2019;Zhu et al 2019b;Xiao et al 2019;Liu et al 2020b;Cheng et al 2014;Zheng et al 2019;Han et al 2020;Li et al 2013;Ge et al 2019;…”

Section: Publicallymentioning

confidence: 99%

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Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

et al. 2022

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Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly embraced by individuals, groups, and organizations as a valuable source of information. This social media generated information comes in the form of tweets or posts, and normally characterized as short text, huge, sparse, and low density. Since many real-world applications need semantic interpretation of such short texts, research in Short Text Topic Modeling (STTM) has recently gained a lot of interest to reveal unique and cohesive latent topics. This article examines the current state of the art in STTM algorithms. It presents a comprehensive survey and taxonomy of STTM algorithms for short text topic modelling. The article also includes a qualitative and quantitative study of the STTM algorithms, as well as analyses of the various strengths and drawbacks of STTM techniques. Moreover, a comparative analysis of the topic quality and performance of representative STTM models is presented. The performance evaluation is conducted on two real-world Twitter datasets: the Real-World Pandemic Twitter (RW-Pand-Twitter) dataset and Real-world Cyberbullying Twitter (RW-CB-Twitter) dataset in terms of several metrics such as topic coherence, purity, NMI, and accuracy. Finally, the open challenges and future research directions in this promising field are discussed to highlight the trends of research in STTM. The work presented in this paper is useful for researchers interested in learning state-of-the-art short text topic modelling and researchers focusing on developing new algorithms for short text topic modelling.

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Section: Discussionmentioning

confidence: 99%

Section: Probabilistic Based Modelsmentioning

confidence: 99%

Section: Specificallymentioning

confidence: 99%

Section: Publicallymentioning

confidence: 99%

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Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

et al. 2022

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“…Perplexity is a common indicator for measuring the quality of language models (45). In the LDA model, the optimal review texts topic number K was determined by perplexity.…”

Section: Topic Numbermentioning

confidence: 99%

Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example

Yang

et al. 2023

Front. Public Health

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IntroductionThroughout the COVID-19 pandemic, many patients have sought medical advice on online medical platforms. Review data have become an essential reference point for supporting users in selecting doctors. As the research object, this study considered Haodf.com, a well-known e-consultation website in China.MethodsThis study examines the topics and sentimental change rules of user review texts from a temporal perspective. We also compared the topics and sentimental change characteristics of user review texts before and after the COVID-19 pandemic. First, 323,519 review data points about 2,122 doctors on Haodf.com were crawled using Python from 2017 to 2022. Subsequently, we employed the latent Dirichlet allocation method to cluster topics and the ROST content mining software to analyze user sentiments. Second, according to the results of the perplexity calculation, we divided text data into five topics: diagnosis and treatment attitude, medical skills and ethics, treatment effect, treatment scheme, and treatment process. Finally, we identified the most important topics and their trends over time.ResultsUsers primarily focused on diagnosis and treatment attitude, with medical skills and ethics being the second-most important topic among users. As time progressed, the attention paid by users to diagnosis and treatment attitude increased—especially during the COVID-19 outbreak in 2020, when attention to diagnosis and treatment attitude increased significantly. User attention to the topic of medical skills and ethics began to decline during the COVID-19 outbreak, while attention to treatment effect and scheme generally showed a downward trend from 2017 to 2022. User attention to the treatment process exhibited a declining tendency before the COVID-19 outbreak, but increased after. Regarding sentiment analysis, most users exhibited a high degree of satisfaction for online medical services. However, positive user sentiments showed a downward trend over time, especially after the COVID-19 outbreak.DiscussionThis study has reference value for assisting user choice regarding medical treatment, decision-making by doctors, and online medical platform design.

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Topic Detection Based on Semantics, Time and Social Relationship

Cheng

Kou

et al. 2019

Lecture Notes in Electrical Engineering

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Building a TIN-LDA Model for Mining Microblog Users’ Interest

Cited by 20 publications

References 27 publications

Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example

Topic Detection Based on Semantics, Time and Social Relationship

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