Emotion computing and Word Mover's Distance

In this paper, we propose an emotion separated method(SeTF・IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF・IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover's Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF・IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field

Exploring Thematic Diversity In News Coverage And Social Media Activity Of Political Candidates Using Unsupervised Machine Learning

The relationship between media and politics has been at the core of communication research for over a century. Previous research has examined the impact of both volume and tone of news coverage of political candidates on their electoral success, and the relationship between the volume of candidates’ social media activity (though not its tone) and electoral success. While past research found a positive relationship between these features and electoral success, recent criticisms have called into question the independent nature of these media factors. Moreover, while past research has paid some attention to volume and tone, researchers have yet to examine other key features of discourse represented in candidates’ coverage as a whole. One such feature is the extent to which a political discourse is unidimensional or multidimensional in nature, referred to in this study as thematic diversity. This is due, in part at least, to the complex nature of thematic diversity making its estimation challenging. Analyzing over 120,000 Tweets written by 142 U.S. Senate candidates during the 2012-2016 election cycles, as well as over 420,000 news articles covering 330 U.S. Senate candidates during the 2008-2016 election cycles, this study systematically explores the relationship between electoral success of political candidates and the volume and tone of their news coverage and social media activity. Using a wide array of controls, this study explores the independent (or dependent) nature of these media features. More importantly, this study goes beyond these previously studied media features, to systematically and empirically explore the relationship between thematic diversity in both candidates’ news coverage and social media activity, and their electoral success. Drawing on the conceptualization of diversity in various fields from biology, to physics and information sciences, and using two unsupervised machine learning methods, semantic network analysis and topic modeling, this study offers a novel approach to the conceptualization and estimation of thematic diversity, accounting for the variety, balance and disparity of various themes in a given corpus. Using these methods, this study offers evidence for a significant, negative, and semi-independent relationship between thematic diversity and electoral success, in both news media and social media

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

A multi-disciplinary co-design approach to social media sensemaking with text mining

This thesis presents the development of a bespoke social media analytics platform called Sentinel using an event driven co-design approach. The performance and outputs of this system, along with its integration into the routine research methodology of its users, were used to evaluate how the application of an event driven co-design approach to system design improves the degree to which Social Web data can be converted into actionable intelligence, with respect to robustness, agility, and usability. The thesis includes a systematic review into the state-of-the-art technology that can support real-time text analysis of social media data, used to position the text analysis elements of the Sentinel Pipeline. This is followed by research chapters that focus on combinations of robustness, agility, and usability as themes, covering the iterative developments of the system through the event driven co-design lifecycle. Robustness and agility are covered during initial infrastructure design and early prototyping of bottom-up and top-down semantic enrichment. Robustness and usability are then considered during the development of the Semantic Search component of the Sentinel Platform, which exploits the semantic enrichment developed in the prototype, alpha, and beta systems. Finally, agility and usability are used whilst building upon the Semantic Search functionality to produce a data download functionality for rapidly collecting corpora for further qualitative research. These iterations are evaluated using a number of case studies that were undertaken in conjunction with a wider research programme, within the field of crime and security, that the Sentinel platform was designed to support. The findings from these case studies are used in the co-design process to inform how developments should evolve. As part of this research programme the Sentinel platform has supported the production of a number of research papers authored by stakeholders, highlighting the impact the system has had in the field of crime and security researc

Unsettling Translation

This collection engages with translation and interpreting from a diverse but complementary range of perspectives, in dialogue with the seminal work of Theo Hermans. A foundational figure in the field, Hermans’s scholarly engagement with translation spans several key areas, including history of translation, metaphor, norms, ethics, ideology, methodology, and the critical reconceptualization of the positioning of the translator and of translation itself as a social and hermeneutic practice. Those he has mentored or inspired through his lectures and pioneering publications over the years are now household names in the field, with many represented in this volume. They come together here both to critically re-examine translation as a social, political and conceptual site of negotiation and to celebrate his contributions to the field. The volume opens with an extended introduction and personal tribute by the editor, which situates Hermans’s work within the broader development of critical thinking about translation from the 1970s onward. This is followed by five parts, each addressing a theme that has been broadly taken up by Theo Hermans in his own work: translational epistemologies; historicizing translation; performing translation; centres and peripheries; and digital encounters. This is important reading for translation scholars, researchers and advanced students on courses covering key trends and theories in translation studies, and those engaging with the history of the discipline

Cultural Heritage Storytelling, Engagement and Management in the Era of Big Data and the Semantic Web

The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production–consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today’s ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities – smart cities;Smart systems for cultural heritage sustainability

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute