Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

European Handbook of Crowdsourced Geographic Information

This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research

European Handbook of Crowdsourced Geographic Information

"This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research.

A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective

Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal challenges for deploying AI models. An unexpected data change brings about severe performance degradation in AI models. We identify two major related research fields, domain shift and concept drift according to the setting of the data change. Although these two popular research fields aim to solve distribution shift and non-stationary data stream problems, the underlying properties remain similar which also encourages similar technical approaches. In this review, we regroup domain shift and concept drift into a single research problem, namely the data change problem, with a systematic overview of state-of-the-art methods in the two research fields. We propose a three-phase problem categorization scheme to link the key ideas in the two technical fields. We thus provide a novel scope for researchers to explore contemporary technical strategies, learn industrial applications, and identify future directions for addressing data change challenges

The role of semantics in enhancing user experience in building and city web applications

This thesis embarks on an exploratory journey through Building Information Modelling (BIM) and City Information Modelling (CIM) within web applications, aiming to significantly uplift citizen engagement and satisfaction. At its core, the thesis proposes an innovative framework that uses semantics to intricately weave contextual information into user experience (UX), fostering innovative applications tailored to the built environment. The intellectual pursuit is meticulously structured around three pivotal research questions, each unfolding a distinct but interconnected inquiry stage. The first research question delves into Enhancing Learning UX with Semantics. It seeks to uncover how semantics can amplify the learning experience within existing web applications. This stage is marked by the development of a semantic web-based mining environment meticulously designed to unravel and map the intricate web of roles and skills pivotal in BIM. The endeavour goes beyond mere identification; it strategically establishes correlations, paving the way for learning pathways tailored and resonant with the evolving dynamics of the built environment. Progressing to the second stage, the thesis casts its investigative net into Context Derivation in Smart Cities. This stage is not just about exploring methods but pioneering ways to extract context from the rich tapestry of static and dynamic artefacts embedded within a Digital Twin framework. The goal? To elevate the UX in smart city applications to unprecedented heights. This stage is characterised by the strategic leveraging of BIM semantics, with the aim of transforming the user experience of a diverse cohort of stakeholders, ranging from architects and urban planners to engineers. It is an endeavour that transcends the conventional, blending advanced methodologies to enrich interactions within the web of smart city ecosystems. The journey culminates with the third research question, which focusses on Semantic Scaling and Social Media Analysis. This stage is visionary in its approach, envisioning the scaling of semantics at the city level and positioning citizens as active sensors in an ever-evolving urban landscape. The ambition is grand – to develop a taxonomy model rooted in a semantic-based risk model. However, the thesis does not stop there; it ventures into the vibrant world of social media data streams. By applying sophisticated natural language processing (NLP) techniques, research meticulously sifts through digital chatter, aiming to uncover hidden narratives that weave together environmental factors, risk events, and the pulse of citizen satisfaction. The findings of this thesis are not only insightful; they are transformative. The research demonstrates the practical applicability of semantics across three core dimensions. In socio organisational aspects, the thesis sheds light on the dynamic nature of construction skills, underscoring the imperative for adaptive training methodologies that keep pace with the rapid evolution of BIM roles. The exploration does not stop at the micro level; it extends its gaze to the macro-grain of the built environment. The thesis showcases the profound impact of advanced web technologies, such as the VueJS front-end framework and innovative web builders. When these technological marvels are harmoniously integrated with core UX principles, they unravel complex phenomena, weaving a tapestry of enhanced UX within the pulsating heart of smart cities. The thesis also pioneers social media analytics, presenting it as a formidable information source that can significantly shape smart city decision making. The insights gleaned are not just data points; they are statistically significant revelations that empower stakeholders, offering them the clarity and foresight to make decisions that are not just informed but visionary. As such, this thesis is not just a scholarly endeavour, but a beacon that illuminates the path for future explorations and developments. It is a testament to the synergistic fusion of information science techniques and smart city communities, significantly contributing to the rapidly evolving landscape of semantic integration and UX enhancement within the built environment. The journey embarked on in this thesis is not just about answering questions; it is about charting new territories, opening new horizons, and setting the stage for a future where the built environment is not just smart, but sentient, responsive, and perpetually in tune with the needs and aspirations of its citizens

A Semantics-based User Interface Model for Content Annotation, Authoring and Exploration

The Semantic Web and Linked Data movements with the aim of creating, publishing and interconnecting machine readable information have gained traction in the last years. However, the majority of information still is contained in and exchanged using unstructured documents, such as Web pages, text documents, images and videos. This can also not be expected to change, since text, images and videos are the natural way in which humans interact with information. Semantic structuring of content on the other hand provides a wide range of advantages compared to unstructured information. Semantically-enriched documents facilitate information search and retrieval, presentation, integration, reusability, interoperability and personalization. Looking at the life-cycle of semantic content on the Web of Data, we see quite some progress on the backend side in storing structured content or for linking data and schemata. Nevertheless, the currently least developed aspect of the semantic content life-cycle is from our point of view the user-friendly manual and semi-automatic creation of rich semantic content. In this thesis, we propose a semantics-based user interface model, which aims to reduce the complexity of underlying technologies for semantic enrichment of content by Web users. By surveying existing tools and approaches for semantic content authoring, we extracted a set of guidelines for designing efficient and effective semantic authoring user interfaces. We applied these guidelines to devise a semantics-based user interface model called WYSIWYM (What You See Is What You Mean) which enables integrated authoring, visualization and exploration of unstructured and (semi-)structured content. To assess the applicability of our proposed WYSIWYM model, we incorporated the model into four real-world use cases comprising two general and two domain-specific applications. These use cases address four aspects of the WYSIWYM implementation: 1) Its integration into existing user interfaces, 2) Utilizing it for lightweight text analytics to incentivize users, 3) Dealing with crowdsourcing of semi-structured e-learning content, 4) Incorporating it for authoring of semantic medical prescriptions

Mobile services for green living

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsUrban cycling is a sustainable transport mode that many cities are promoting. However, few cities are taking advantage of geospatial technologies to represent and analyse behavioural patterns and barriers faced during cycling. This thesis is within the fields of geoinformatics and serious games, and the motivation came from our desire to help both citizens and cities to better understand cyclist behaviour and mobility patterns. We attempted to learn more about the impact of gamified strategies on engagement with cycling, the reasons for choosing between mobile cycling applications and the way such applications would provide commuting information. Furthermore, we explored the potential benefits of offering tools to build decision-making for mobility more transparent, to increase cycling data availability, and to analyse commuting patterns. In general, we found our research useful to enhance green living actions by increasing citizens’ willingness to commute by bicycle or communicating cycling conditions in cities. For urban cycling, data coming from mobile phones can provide a better assessment and enrich the analysis presented in traditional mobility plans. However, the diversity of current mobile applications targeting cyclists does not provide useful data for analysing commuter (inner-city, non-sporting) cycling. Just a few cyclists are adopting these applications as part of their commuting routine, while on the other hand cities are lacking a valuable source of constantly updated cycling information helpful to understand cycling patterns and the role of bicycles in urban transport. This thesis analyses how the incentives of location-based games or geo-games might increase urban cycling engagement and, through this engagement, crowdsource cycling data collection to allow cities to better comprehend cycling patterns. Consequently, the experiment followed a between-groups design to measure the impact of virtual rewards provided by the Cyclist Geo-c application on the levels of intention, satisfaction, and engagement with cycling. Then, to identify the frictions which potentially inhibit bicycle commuting, we analysed the bicycle trips crowdsourced with the geo-game. Our analysis relied on a hexagonal grid of 30-metre cell side to aggregate trip trajectories, calculate the friction intensity and locate the frictions. The thesis reports on the results of an experiment which involved a total of 57 participants in three European cities: M¨unster (Germany), Castell ´o (Spain), and Valletta (Malta). We found participants reported higher satisfaction and engagement with cycling during the experiment in the collaboration condition. However, we did not find a significant impact on the participants’ worldview when it comes to the intentions to start or increase cycling. The results support the use of collaboration-based rewards in the design of game-based applications to promote urban cycling. Furthermore, we validated a procedure to identify not only the cyclists’ preferred streets but also the frictions faced during cycling analysing the crowdsourced trips. We successfully identified 284 places potentially having frictions: 71 in M¨unster, Germany; 70 in Castell ´ o, Spain; and 143 in Valletta, Malta. At such places, participants recorded trip segments at speeds below 5 Km/h indicating a deviation from a hypothetical scenario with a constant cycling speed. This thesis encompasses the cyclist and city perspectives of offering virtual incentives in geo-games and crowdsourcing cycling data collection to better comprehend cycling conditions in cities. We also compiled a set of tools and recommendations for researchers, practitioners, mobile developers, urban planners and cyclist associations interested in fostering sustainable transport and the use of bicycles

VISUAL ANALYTICS FOR OPEN-ENDED TASKS IN TEXT MINING

Overview of documents using topic modeling and multidimensional scaling is helpful in understanding topic distribution. While we can spot clusters visually, it is challenging to characterize them. My research investigates an interactive method to identify clusters by assigning attributes and examining the resulting distributions. ParallelSpaces examines the understanding of topic modeling applied to Yelp business reviews, where businesses and their reviews each constitute a separate visual space. Exploring these spaces enables the characterization of each space using the other. However, the scatterplot-based approach in ParallelSpaces does not generalize to categorical variables due to overplotting. My research proposes an improved layout algorithm for those cases in our follow-up work, Gatherplots, which eliminate overplotting in scatterplots while maintaining individual objects. Another limitation in clustering methods is the fixed number of clusters as a hyperparameter. TopicLens is a Magic Lens-type interaction technique, where the documents under the lens are clustered according to topics in real time. While ParallelSpaces help characterize the clusters, the attributes are sometimes limited. To extend the analysis by creating a custom mixture of attributes, CommentIQ is a comment moderation tool where moderators can adjust model parameters according to the context or goals. To help users analyze documents semantically, we develop a technique for user-driven text mining by building a dictionary for topics or concepts in a follow-up study, ConceptVector, which uses word embedding to generate dictionaries interactively and uses those dictionaries to analyze the documents. My dissertation contributes interactive methods to overview documents to integrate the user in text mining loops that currently are non-interactive. The case studies we present in this dissertation provide concrete and operational techniques for directly improving several state-of-the-art text mining algorithms. We summarize those generalizable lessons and discuss the limitations of the visual analytics approach