Celebrating European law and economics: three decades in a long tradition

Trade and...Problems, Cost-Benefit Analysis and Subsidiarity

Social media and in particular, microblogs are becoming an important data source for disease surveillance, behavioral medicine, and public healthcare. Topic Models are widely used in microblog analytics for analyzing and integrating the textual data within a corpus. This paper uses health tweets as microblogs and attempts the health data clustering by topic models. The traditional topic models, such as Latent Semantic Indexing (LSI), Probabilistic Latent Schematic Indexing (PLSI), Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and integer Joint NMF(intJNMF) methods are used for health data clustering; however, they are intractable to assess the number of health topic clusters. Proper visualizations are essential to extract the information from and identifying trends of data, as they may include thousands of documents and millions of words. For visualization of topic clouds and health tendency in the document collection, we present hybrid topic models by integrating traditional topic models with VAT. Proposed hybrid topic models viz., Visual Non-negative Matrix Factorization (VNMF), Visual Latent Dirichlet Allocation (VLDA), Visual Probabilistic Latent Schematic Indexing (VPLSI) and Visual Latent Schematic Indexing (VLSI) are promising methods for accessing the health tendency and visualization of topic clusters from benchmarked and Twitter datasets. Evaluation and comparison of hybrid topic models are presented in the experimental section for demonstrating the efficiency with different distance measures, include, Euclidean distance, cosine distance, and multi-viewpoint cosine similarity.

Topic model is an increasing useful tool to analyze the semantic level meanings and capture the topical features. However, there is few research about the comparative study of the topic models. In this paper, we describe our comparative study of three topic models in the extrinsic application of topic clustering. The topic model distance is defined on the converged parameters of topic models, which is used in the topic clustering. Then, the topic models are compared using the clustering result of the corresponding topic distance matrix. A series of comparative experiments are carried on a corpus containing 5033 web news from 30 topics using the cosine distance as the base-line. Web page collections with different number of topics and documents are used in experiments. The experiment results show that topic clustering using topic distance achieves a better precision and recall in the data set containing related topics. The topic clustering using topic distance benefits from the topic features captured by topic models. The complex topic model does provide further help than the simple topic model in topic clustering.

Qualitative comparative institutional analysis of environmental governance: Implications from research on payments for ecosystem services

Cross-Border Insolvency Law: A Comparative Institutional Analysis

The Varieties of Comparative Institutional Analysis

Evaluation of clustering and topic modeling methods over health-related tweets and emails

Topic modeling has been widely used for decades across various applications, particularly in web data analysis. The Latent Dirichlet Allocation (LDA) is based on statistical modeling, where words are modeled as a distribution of words, and documents as a distribution of topics. Today, there are several text ways to represent texts, from the simplest one Bag-of-Words to the recent word embedding. Two main classes of word embeddings exist, static word embeddings (Word2Vec, Glove, etc) and contextual word embedding (BERT, RoBERTa, etc). And, some recent works, showed the interest and the effectiveness of integrating such text representations to improve topic modeling performance. However, in the unsupervised context of topic modeling, there is no prior knowledge that can ensure the effectiveness of a specific text representation. Also, they often face the challenge of topic collapsing, where identified topics become semantically redundant, resulting in overly similar topics, limited topic diversity, and reduced interpretability of the model. In this paper, we propose a new topic modeling approach that can consider multi-text representations simultaneously named Multi-view Topic Modeling (MTM). The MTM algorithm is able to discover topics based on several text representations and generate multi-view topic embeddings allowing us to interpret the results considering several views. We evaluate the MTM algorithm on five real-world datasets and show the effectiveness of the proposed algorithm in terms of topic coherences and clustering.

Global governance is essentially about governance. That is, it is about those mechanisms that make societal or global determinations. Comparative institutional analysis is by its nature focused on governance and governance mechanisms and understanding institutional behavior lies in the dynamics of participation– the bottom-up forces that determine who is influential and who is not. In turn, the dynamics of participation is dependent in turn on the costs and benefits of participation. The works in this book attempt to establish and grow comparative institutional analysis as a general analytical framework for organizing the issues of global governance. The first chapter exams the basic constitutional issues faced by global governance. The second expands these insights to a general framework to analyze global governance. The third explores global governance and the use of comparative institutional analysis in the context of environmental issues. The fourth explores the institutional choice issues raised by trade and more broadly global public goods. The fifth examines what comparative institutional analysis of various sorts tell us about globalization and the role of law. Although this book sets out few answers, it does propose a route to a common understanding of the problems and with it a way to reach meaningful answers.

Our goal is to exploit a unified language model so as to explain the generative process of documents precisely in view of their semantic and topic structures. Because various methods model documents in disparate ways, we are motivated by the expectation that coordinating these methods will allow us to achieve this goal more efficiently than using them in isolation; we combine topic models, embedding models, and neural language models. As we focus on the fact that topic models can be shared among, and indeed complement embedding models and neural language models, we propose Word and topic 2 vec (Wat2vec), and Topic Structure-Aware Neural Language Model (TSANL). Wat2vec uses topics as global semantic information and local semantic information as embedding representations of topics and words, and embeds both words and topics in the same space. TSANL uses recurrent neural networks to capture long-range dependencies over topics and words. Since existing topic models demand time consuming learning and have poor scalability, both due to breaking the document?s structure such as order of words and topics, TSANL maintains the orders of words and topics as phrases and segments, respectively. TSANL reduces the calculation cost and required memory by feeding topic recurrent neural networks, and topic specific word networks with these embedding representations. Experiments show that TSANL maintains both segments and topical phrases, and so enhances previous models.

Twitter is a popular social media for every user to issue thoughts and emotional forms which are tweets, tweets that only have 140 characters with limitations to write in text. Twitter is one of the social media places to get information that is always up to date, tweets are categorized into big data because tweets are information that can be used as a source of data for research. Latent Dirichlet Allocation (LDA) as an algorithm that can process large text data (big data). In this study using the LDA method as an algorithm to produce topic modeling, each topic similarity, and visualization of topic clusters from the tweet data generated as many as 4 topics (Economic, Military, Sports, Technology) in Indonesian, where each topic has a number different tweets. The LDA method used in the processing of tweet data is successfully carried out and works optimally, in each topic extraction, topic modeling, generating index words that are in each topic cluster and computer visualization in the topic.LDA output shows optimal performance in the process of word indexing in Sport topics with 1260 tweets with an accuracy of 98% better than the LSI method in Topic Modeling.

The central hypothesis of this paper is that there may be situations in which the traditional approach to institutional analysis is of limited applicability. Such an approach, which has been called 'comparative institutional analysis', consists of comparing institutional environments and institutional arrangements in terms of specific economic or other efficiency criteria to see which one performs better. However, because of limitations to accurately predict the future performance of alternative institutional settings, comparisons are not always possible. Furthermore, in most cases the only information available is the performance of the current institutional setting. To account for this methodological deficiency, a generic methodology for institutional analysis, which consists of four steps (institutional structure, institutional efficiency, institutional choice, and institutional change), is proposed in this paper. Accordingly, the emphasis switches from evaluating alternative institutional choices to improving current scenarios. To show the validity of this methodology, some results of its application to a case study are presented. Although more research on this four-step methodology is needed, it proved to be robust when applied to the analysis of the governance of irrigated agriculture in the Peninsula of Santa Elena, Ecuador.

Two important methodological debates in political theory concern (i) the place of historical research and interpretation in normative inquiry; and (ii) the importance of comparing different cultural traditions of political thought. The first question animates a long-standing controversy over the relative importance of ‘history of political thought’ versus ‘philosophy’ in political theory, while the second has been central to the recent growth of ‘comparative political theory’. Despite both debates being concerned, in fundamental ways, with history and comparison, they have remained surprisingly disconnected from broader debates in the humanities and social sciences about the use of comparative history and, in particular, of the prominent methodological approach in political science known as ‘comparative historical analysis’. This may reflect the fact that the debates over history of political thought and comparative political theory have fundamentally revolved around thought—history as the history of thought and comparison as the comparison of thought—whereas comparative historical analysis is more centrally focused on the study of political behaviour. In this paper I suggest that this neglect of comparative historical analysis in political theory represents a missed opportunity. Like other forms of social scientific inquiry, comparative historical analysis can yield important empirical knowledge for political theorists. But I wish to more ambitiously suggest that comparative historical analysis can also be adapted for conducting normative political theory itself. I summarise the key features of comparative historical analysis for political theorists, and explain how its use of historical cases can supplement both the use of imaginary thought experiments and the study of contemporary politics. I then delineate three specific ways in which comparative historical analysis might be used to support normative inquiry: deductive testing, inductive construction, and casuistic elaboration. All three, I argue, can help meet recent calls to bring the study of real political behaviour more centrally into political theory research.

This paper examines the core features of Masahiko Aoki’s comparative institutional analysis (CIA), focusing on its methodology and institutional conceptualization. Aoki’s CIA integrates institutional and policy theory with comparative and historical analysis to explain institutional diversity and co-evolution. Departing from market-centric models, it emphasizes interdependencies among corporations, government, and society, as well as the roles of public representations and shared beliefs. Drawing on Aoki’s English and Japanese works, the paper situates CIA within his intellectual history and offers a preliminary comparison with the institutional theories of Ronald Coase, Douglass North, and Oliver Williamson. It also outlines five areas for future research, including the landscape of institutional economics, firm and corporate institutions, tech monopolies, Japan’s institutional transition and dynamic capabilities, and the co-evolution of human nature and institutions. Nearly a decade after Aoki’s passing, the paper argues that CIA remains essential for advancing institutional economics in today’s complex global landscape.

Topic models have been widely utilized in Topic Detection and Tracking tasks, which aim to detect, track, and describe topics from a stream of broadcast news reports. However, most existing topic models neglect semantic or syntactic information and lack readable topic descriptions. To exploit semantic and syntactic information, Language Models (LMs) have been applied in many supervised NLP tasks. However, there are still no extensions of LMs for unsupervised topic clustering. Moreover, it is difficult to employ general LMs (e.g., BERT) to produce readable topic summaries due to the mismatch between the pretraining method and the summarization task. In this paper, noticing the similarity between content and summary, first we propose a Language Model-based Topic Model (LMTM) for Topic Clustering by using an LM to generate a deep contextualized word representation. Then, a new method of training a Topic Summarization Model is introduced, where it is not only able to produce brief topic summaries but also used as an LM in LMTM for topic clustering. Empirical evaluations of two different datasets show that the proposed LMTM method achieves better performance over four baselines for JC, FMI, precision, recall and F1-score. Additionally, the generated readable and reasonable summaries also validate the rationality of our model components.