An Analysis of Public Opinions Regarding Take-Away Food Safety: A 2015–2018 Case Study on Sina Weibo

Song, Cen; Guo, Chunyu; Hunt, Kyle; Zhuang, Jun

doi:10.3390/foods9040511

Cited by 27 publications

(16 citation statements)

References 23 publications

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“…Rauchfleisch and Schäfer (2015) have identified seven public spheres on Weibo in terms of the topic per se (thematic, encoded and meta public spheres), the scope of topics being discussed (local, non-domestic public spheres), the mode of delivery (mobile public spheres) and the longevity of topics (short term public spheres). For instance, in thematic public spheres, netizens discuss apolitical life-world problems, such as environmental pollution (Ji et al, 2018) and food safety (Song et al, 2020). Netizens may also engage in discussion of non-domestic issues, such as the 2020 US presidential election.…”

Section: Online Public Opinion Expressionmentioning

confidence: 99%

Who should apologise: Expressing criticism of public figures on Chinese social media in times of COVID-19

Tao

2021

Discourse & Society

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Previous studies on public opinion expression in communication, political science and discourse analysis are restricted to a censorship-/counter-censorship frame and focus their analysis on events with political agendas. This study explores netizens’ discursive practice by focusing the analysis on netizens’ language use in context per se, rather than from a censorship/counter-censorship viewpoint. It adopts a discursive pragmatic approach to examine a ‘mundane’ trending topic regarding a dispute between two public figures rather than ‘major’ events with acute social and political agendas. This study present evidence that Weibo users criticise public figures through indirect discursive strategies, including parody of name, constructed dialogues and rhetorical questions. It also highlights two prominent sentiments in Weibo public spheres during the COVID-19 pandemic – cyber nationalism and binary opposition between China and the rest of the world. The online backlash against Fang demonstrates how easily netizens can change their views towards a certain event.

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Section: Online Public Opinion Expressionmentioning

confidence: 99%

Who should apologise: Expressing criticism of public figures on Chinese social media in times of COVID-19

Tao

2021

Discourse & Society

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“…The LDA topic model is a representative model in text mining, which can identify topics in documents and mine the hidden information in the corpus to extract potential topics and has been widely used in text corpus topic discovery [35,36]. Applying the topic model method to the topic mining of the university network opinion text corpus can help to understand the main ideas among the huge number of opinions at the text level according to the idea of topics [37,38]. Based on this, this paper uses the LDA topic model for topic mining of university public comment data.…”

Section: Analysis Of Students' Emotional Evolution In Time Contextmentioning

confidence: 99%

Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on the Official Account of Chinese Universities

Tian

2021

Electronics

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With the advent of the new media mobile Internet era, the network public opinion in colleges and universities, as an extension of social network public opinion, is also facing a crisis in the prevention, control, and governance system. In this paper, the Fiddler was used to collect the comments and other relevant data of the COVID-19 topic articles on the WeChat Official Accounts of China’s top ten universities in 2020. The BILSTM_LSTM sentiment analysis model was used to analyze the sentiment tendency of the comments, and the LDA topic model was used to mine the topics of the comments with different emotional attributes at different stages of COVID-19. Based on sentiment analysis and text mining, entities and relationships in the theme graph of public opinion events in colleges and universities were identified, and the Neo4j graph database was established to construct the sentimental knowledge graph of the pandemic theme of university public accounts. People’s attitudes in university public opinion are easily influenced by a variety of factors, and the degree of emotional disposition changes over time, with the stage the pandemic is in, and with different commentators; official account opinion topics change with the development of the time stage of the pandemic, and students’ positive and negative comment topics show a diverse trend. By incorporating topic mining into the sentimental knowledge graph, the graph can realize functions such as the emotion retrieval of comments on university public numbers, a source search of security threats in university social networks, and monitoring of comments on public opinion under the theme of the pandemic, which provides new ideas for further exploring the research and governance system of university network public opinion and is conducive to preventing and resolving campus public opinion crises.

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“…Alaei et al [18] applied different approaches to the sentiment analysis of tourism reviews, including machine learning, dictionary-based, semantic, and hybrid approaches. Song et al [19] analyzed and mined users' sentiments and opinions using natural language processing, after which latent Dirichlet allocation (LDA) and kmeans were used to extract and cluster topics from the posts on take-away food safety. Chintalapudi et al [20] implemented text mining in seafarers' medical documents and performed sentimental analysis by adopting both lexicon and naïve Bayes' algorithms to generate knowledge of medical issues.…”

Section: Sentiment Analysis Researchmentioning

confidence: 99%

A Hybrid Recommendation Approach for Viral Food Based on Online Reviews

Song

José

et al. 2021

Foods

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Nowadays, there are many types of viral foods and consumers expect to be able to quickly find foods that meet their own tastes. Traditional recommendation systems make recommendations based on the popularity of viral foods or user ratings. However, because of the different sentimental levels of users, deviations occur and it is difficult to meet the user’s specific needs. Based on the characteristics of viral food, this paper constructs a hybrid recommendation approach based on viral food reviews and label attribute data. A user-based recommendation approach is combined with a content-based recommendation approach in a weighted combination. Compared with the traditional recommendation approaches, it is found that the hybrid recommendation approach performs more accurately in identifying the sentiments of user evaluations, and takes into account the similarities between users and foods. We can conclude that the proposed hybrid recommendation approach combined with the sentimental value of food reviews provides novel insights into improving the existing recommendation system used by e-commerce platforms.

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An Analysis of Public Opinions Regarding Take-Away Food Safety: A 2015–2018 Case Study on Sina Weibo

Cited by 27 publications

References 23 publications

Who should apologise: Expressing criticism of public figures on Chinese social media in times of COVID-19

Who should apologise: Expressing criticism of public figures on Chinese social media in times of COVID-19

Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on the Official Account of Chinese Universities

A Hybrid Recommendation Approach for Viral Food Based on Online Reviews

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