A Local-Global LDA Model for Discovering Geographical Topics from Social Media

Qiang, Siwei; Wang, Yongkun; Jin, Yaohui

doi:10.1007/978-3-319-63579-8_3

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…At the level of social media methods, the existing research reserves are very rich. Qiang et al [41] proposed a geographical topic modeling method based on the Latent Dirichlet Allocation (LDA) model to find topics that start at a specific time. Munuswamy et al [42] gave a new sentiment analysis rating prediction method and generated a new recommendation system.…”

Section: Social Media Analytical Methodsmentioning

confidence: 99%

A WeChat Official Account Reading Quantity Prediction Model Based on Text and Image Feature Extraction

Bai

2022

IEEE Access

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This paper describes a study that built a neural network prediction model based on feature extraction, focusing on text analysis and image analysis of WeChat official accounts reading quantity. Based on the embedding method of the deep learning model, we extracted the text features in the title and the image features in the cover picture, explored the relationship between these features and the reading quantity, and built a neural network model based on these features to predict the reading quantity. The results show that there is a phenomenon of sentiment fusion in the text, and a sentence vector model based on Doc2Vec and a neural network model both had a good performance. This paper proposes a tool that can predict the reading quantity in advance and help administrators adjust the titles and images according to the predicted results. INDEX TERMSFeature extraction; neural network; WeChat official accounts; Doc2Vec; user engagement I. INTRODUCTIONSocial media platforms, such as Twitter and Facebook, provide opportunities for people to create, communicate, and share ideas. In China, WeChat is a social media platform with strong communication and influencing characteristics. Administrators apply for WeChat official accounts to publish different kinds of articles or news on the platform, and readers can obtain and share information. By November 2017, WeChat had gathered more than 10 million official accounts, including 3.5 million monthly active official accounts and 797 million monthly active users [1]. Many authors have formed their own brands through original articles and become entrepreneurs on WeChat.Previous research on WeChat has focused on user behaviors and attitudes as well as the influence mechanism and communication power of WeChat as a social media platform. Specifically, it involves user satisfaction, user attitude, user intention [2-5], user engagement behavior [6][7], and the influence mechanism and effect of WeChat as an information communication platform on service provided by users [8][9][10][11]. However, there has been little research on deeper mining and exploration of the text through natural language processing (NLP). Moreover, as far as we know, there has been little research on the analysis of the cover image of WeChat official accounts. Our study focuses on text analysis and image analysis of WeChat official accounts reading quantity, which contributes to the research in this specific field and addresses

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Section: Social Media Analytical Methodsmentioning

confidence: 99%

A WeChat Official Account Reading Quantity Prediction Model Based on Text and Image Feature Extraction

Bai

2022

IEEE Access

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“…Nesting in document collections and local topics have been studied in different data settings (Chang and Blei, 2009;Rosen-Zvi et al, 2004;Yang et al, 2016;Qiang et al, 2017;Chemudugunta et al, 2006;Hua et al, 2020). We discuss the similarities and differences in the context of web pages nested in web sites.…”

Section: Introductionmentioning

confidence: 99%

“…We do not need to model links between web pages. Some models for nested document collections address nesting by modeling multiple levels of document-topic distributions, but do not explicitly model local topics and their topic-word distributions (Qiang et al, 2017). Under the author model in Rosen-Zvi et al (2004), local topics are explicitly modeled; however, global topics are not modeled.…”

Section: Introductionmentioning

confidence: 99%

Local and Global Topics in Text Modeling of Web Pages Nested in Web Sites

Wang¹,

Weiss²

2021

Preprint

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Topic models are popular models for analyzing a collection of text documents. The models assert that documents are distributions over latent topics and latent topics are distributions over words. A nested document collection is where documents are nested inside a higher order structure such as stories in a book, articles in a journal, podcasts within an author, or web pages in a web site. In a single collection of documents, topics are global, that is, shared across all documents. For web pages nested in web sites, topic frequencies will likely vary from web site to web site. Within a web site, topic frequencies will almost certainly vary from web page to web page. A hierarchical prior for topic frequencies models this hierarchical structure and specifies a global topic distribution. Web site topic distributions then vary around the global topic distribution and web page topic distributions vary around the web site topic distribution. In a nested collection of web pages, some topics are likely unique to a single web site.Local topics in a nested collection of web pages are topics unique to one web site. For United States local health department web sites, even brief inspection of the text shows local geographic and news topics specific to each health department that are not present in other web sites. Regular topic models that ignore the nesting structure may identify local topics, but do not label those topics as local nor

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Author topic model for co-occurring normal documents and short texts to explore individual user preferences

Yang

Wang

2021

Information Sciences

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A Local-Global LDA Model for Discovering Geographical Topics from Social Media

Cited by 4 publications

References 22 publications

A WeChat Official Account Reading Quantity Prediction Model Based on Text and Image Feature Extraction

A WeChat Official Account Reading Quantity Prediction Model Based on Text and Image Feature Extraction

Local and Global Topics in Text Modeling of Web Pages Nested in Web Sites

Author topic model for co-occurring normal documents and short texts to explore individual user preferences

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