Policy Text Analysis Based on Text Mining and Fuzzy Cognitive Map

Han, Huiijan; Wang, Qinghai; Chen, Caixian

doi:10.1109/cis.2019.00038

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Han et al ( 2019) used text mining, potential semantic analysis, and other technologies to extract policy elements with the relevant policies of state-owned capital layout and reorganization. The non-interventionist approach avoids the validity flaws caused by the subjective bias of researchers and provides more objective policy recommendations for policymakers from a neutral perspective [3]. Miao et al (2021) used text mining technology to construct a policy evaluation model using logistics policies from three Chinese provinces.…”

Section: Text Mining On Policies and Topic Modelmentioning

confidence: 99%

Quantitative Analysis of Food Safety Policy—Based on Text Mining Methods

Song

Gholizadeh

Zhuang

2022

Foods

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Recently, food safety and cold chain food have been closely related to the epidemic. The party and the state have intensified efforts to solve food safety problems and prevent possible epidemic risks. China has issued a series of policies and plans to strengthen food safety supervision to improve the food safety policy system. To our knowledge, little work has studied policy problems of food safety with in-depth quantitative analysis for an extended period. In accordance with the different national policies and regulations for food safety, this paper fills the gap by analyzing the policies and comparing the central and local policies issued in China from 2007–2022. In addition, the Latent Dirichlet Allocation (LDA) topic model and K-Means clustering model are constructed to analyze the content of food safety policies and identify hot topics. Finally, a quantitative analysis of China’s food safety policies is made from four aspects: the number of policy release years, the distribution area, the range of action, and the affiliated institutions. The results show that: (a) there is a partial surge in food safety policies issued in 2007, 2011, and 2017; (b) the local food safety policy has a high inheritance to the central policy content, and the trends of annual publication number are highly consistent; (c) the innovation of different policy contents in the region have their own characteristics; (d) the proportion of compulsory and capacity policies is much more significant than that of other types of policies. This paper provides some novel insights into food safety policies.

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Section: Text Mining On Policies and Topic Modelmentioning

confidence: 99%

Quantitative Analysis of Food Safety Policy—Based on Text Mining Methods

Song

Gholizadeh

Zhuang

2022

Foods

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“…The approaches to causality detection can be broadly classi ed into (top-down) co-occurrence-based methods and (bottom-up) causal relation extraction methods. Co-occurrence-based methods reduce a large volume of text into core concepts and then identify connections among these concepts (Han et al, 2019;Kim et al, 2016;Son et al, 2020). Causal relation extraction methods identify the connections within the text and then aggregate them (Asghar, 2016;Bach & Badaskar, 2007;Khoo & Na, 2006).…”

Section: Introductionmentioning

confidence: 99%

A semi-automated approach to policy-relevant evidence synthesis: Combining natural language processing, causal mapping, and graph analytics for public policy

Hooper,

Goyal,

Blok

et al. 2023

Preprint

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Although causal evidence synthesis is critical for the policy sciences – whether it be analysis for policy or analysis of policy – its repeatable, systematic, and transparent execution remains challenging due to the growing volume, variety, and velocity of policy-relevant evidence generation as well as the complex web of relationships within which policies are usually situated. To address these shortcomings, we developed a novel, semi-automated approach to synthesizing causal evidence from policy-relevant documents. Specifically, we propose the use of natural language processing (NLP) for the extraction of causal evidence and subsequent homogenization or normalization of the varied text, causal mapping for the collation, visualization, and summarization of complex interdependencies within the policy system, and graph analytics for further investigation of the structure and dynamics of the causal map. We illustrate this approach by applying it to a collection of 28 articles on the emissions trading scheme (ETS), a policy instrument of increasing importance for climate change mitigation. In all, we find 300 variables and 284 cause-effect pairs in our input dataset (consisting of 4524 sentences), which are reduced to 70 unique variables and 119 cause-effect pairs after normalization. We create a causal map depicting these and analyze it subsequently to obtain systemic perspective as well as policy-relevant insight on the ETS that is broadly consistent with select manually conducted, previous meta-reviews of the policy instrument. We conclude that, despite its present limitations, this approach can help synthesize causal evidence for policy analysis, policymaking, and policy research.

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Analyzing society anti-vaccination attitudes towards COVID-19: combining latent dirichlet allocation and fuzzy association rule mining with a fuzzy cognitive map

Eligüzel

2023

Fuzzy Optim Decis Making

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COVID-19 has been declared a pandemic and countries are tackling this disease either through preventative measures such as lockdown and sanitization or through curative ones such as medication, isolation, and so on. Some people believe that vaccination is the best way to prevent this disease, while others disagree. Society’s attitudes toward vaccination can be influenced by a variety of factors such as misunderstanding, ambiguity, lack of knowledge. The proposed study’s goal is to better understand people’s attitudes regarding vaccination by focusing on key topics related to COVID-19 anti-vaccine tweets. Tweets are obtained over a period based on the number of COVID-19 cases by utilizing the “anti-vaccine” keyword rather than the “vaccine” keyword. Furthermore, in addition to people perceptions and attitudes toward anti-vaccination, the causal relationship between each topic is investigated. As a result, latent dirichlet allocation (LDA), fuzzy association rule mining (FARM), fuzzy cognitive map (FCM), and fuzzy c-means are used to conduct a complete study. Topics are analyzed independently using clustering and scenario analysis. The findings demonstrate the most common topics in anti-vaccination tweets, as well as the influence of each topic on the others.

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Policy Text Analysis Based on Text Mining and Fuzzy Cognitive Map

Cited by 5 publications

References 4 publications

Quantitative Analysis of Food Safety Policy—Based on Text Mining Methods

Quantitative Analysis of Food Safety Policy—Based on Text Mining Methods

A semi-automated approach to policy-relevant evidence synthesis: Combining natural language processing, causal mapping, and graph analytics for public policy

Analyzing society anti-vaccination attitudes towards COVID-19: combining latent dirichlet allocation and fuzzy association rule mining with a fuzzy cognitive map

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