Extracting topic keywords from Sina Weibo text sets

Xu, Shihong; Guo, Juncai; Chen, Xue

doi:10.1109/icalip.2016.7846663

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The experiment results are presented in Table 5. To fully observe the differences between the key word extraction methods, we assess the P, R, and Fmeasure values under different Ns, where N indicates the first N key words extracted, and the range of N is from [1] to [10]. Figure 2 shows a comparison of the accuracy rate, P, of the various models.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…It has been widely studied because of its large amount of data and rich information. Xu [10] used Sina Weibo data as the research object for key word extraction tasks, implemented a method that combines the merits of LDA, Entropy and TextRank, and considered the influence of the topic discrimination of the extracted key word. In microblogging environments where research on topic models of short texts has gradually become a hot topic and a difficult task [11], researchers explore topic models for short messages.…”

Section: Related Workmentioning

confidence: 99%

“…Xu [10] used Sina Weibo data as the research object for key word extraction tasks, implemented a method that combines the merits of LDA, Entropy and TextRank, and considered the influence of the topic discrimination of the extracted key word. In microblogging environments where research on topic models of short texts has gradually become a hot topic and a difficult task [11], researchers explore topic models for short messages. Önal [12] proposed an LSA-based key word extraction method that can identify semantic information in topic words or key words using LSA, and the results perform well in large document sets.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Key word extraction for short text via word2vec, doc2vec, and textrank

Li¹,

Huang²,

Fan³

et al. 2019

Turk J Elec Eng & Comp Sci

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The rapid development of social media encourages people to share their opinions and feelings on the Internet.Every day, a large number of short text comments are generated through Twitter, microblogging, WeChat, etc., and there is high commercial and social value in extracting useful information from these short texts. At present, most studies have focused on extracting text key words. For example, the LDA topic model has good performance with long texts, but it loses effectiveness with short texts because of the noise and sparsity problems. In this paper, we attempt to use Word2Vec and Doc2Vec to improve short-text key word extraction. We first added the method of the collaborative training of word vectors and paragraph vectors and then used the TextRank model's clustering nodes. We adjusted the weights of the key words that were generated by computing the jump probability between nodes and then obtained the node-weighted score, and eventually sorted the generated key words. The experimental results show that the improved method has good performance on the datasets.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Key word extraction for short text via word2vec, doc2vec, and textrank

Li¹,

Huang²,

Fan³

et al. 2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

Extraction and Sequencing of Keywords from Twitter

Singh

Kumar

Aggarwal

2018

Advances in Intelligent Systems and Computing

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Malaysian Chinese folk beliefs on Facebook based on LDA topic modelling

Hu,

Ho,

Fan

2024

Humanit Soc Sci Commun

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In the digital age, as social media evolves into a new and significant centre for the dissemination of Chinese folk beliefs, the Malaysian Chinese have actively shared information about these folk beliefs on their social media platforms. The dissemination has transcended regional barriers, encouraging more Malaysian Chinese across various states to actively participate in public discussions on this topic. This study delves into Malaysian Chinese folk beliefs by analysing data from Facebook. A comprehensive examination of 4012 text posts was conducted using the latent Dirichlet allocation (LDA) model for topic modelling. The analysis identified four main themes on social media: ‘Practitioners Worship’, ‘Temple Activities’, ‘Deity Legends’, and ‘Merchandise about Deity Statues’. Based on integrating social construction theory and media ecology theory, the study first explores the varied constructors, including practitioners, temple organisations, media organisations, and merchants. Secondly, Malaysian Chinese folk beliefs on social media present characteristics of utilitarianism, regional diversity, multiple social functions, flowing realms, strong Taoist elements, commercialisation, and a close relationship with the Spring Festival. Furthermore, ‘Safety and Peace’, ‘Pray for Demands’, and ‘Merits and Virtues’ form an interconnected semantic nexus. Hence, the findings theoretically highlight the interaction and significance of social media in the construction and practice of folk beliefs within the Malaysian Chinese community. Practically, this research provides valuable insights into the understanding and dissemination of Malaysian Chinese religious culture in the digital era.

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Extracting topic keywords from Sina Weibo text sets

Cited by 3 publications

References 9 publications

Key word extraction for short text via word2vec, doc2vec, and textrank

Key word extraction for short text via word2vec, doc2vec, and textrank

Extraction and Sequencing of Keywords from Twitter

Malaysian Chinese folk beliefs on Facebook based on LDA topic modelling

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