Exploring user experience with image schemas, sentiments, and semantics

Although the concept of user experience includes two key aspects, experience of meaning (usability) and experience of emotion (affect), the empirical work that measures both the usability and affective aspects of user experience is currently limited. This is particularly important considering that affect could significantly influence a user’s perception of usability. This paper uses image schemas to quantitatively and systematically evaluate both these aspects. It proposes a method for evaluating user experience that is based on using image schemas, sentiment analysis, and computational semantics. The aim is to link the sentiments expressed by users during their interactions with a product to the specific image schemas used in the designs. The method involves semantic and sentiment analysis of the verbal responses of the users to identify (i) task-related words linked to the task for which a certain image schema has been used and (ii) affect-related words associated with the image schema employed in the interaction. The main contribution is in linking image schemas with interaction and affect. The originality of the method is twofold. First, it uses a domain-specific ontology of image schemas specifically developed for the needs of this study. Second, it employs a novel ontology-based algorithm that extracts the image schemas employed by the user to complete a specific task and identifies and links the sentiments expressed by the user with the specific image schemas used in the task. The proposed method is evaluated using a case study involving 40 participants who completed a set task with two different products. The results show that the method successfully links the users’ experiences to the specific image schemas employed to complete the task. This method facilitates significant improvements in product design practices and usability studies in particular, as it allows qualitative and quantitative evaluation of designs by identifying specific image schemas and product design features that have been positively or negatively received by the users. This allows user experience to be assessed in a systematic way, which leads to a better understanding of the value associated with particular design features

Evaluating intuitive interactions using image schemas

Intuitive use is a desirable feature in interface design. In the last decade, researchers have made considerable progress in developing approaches for design for intuitive use. Most of the current approaches, however, have been limited to qualitative exploration, in the form of providing guidelines aimed to help designers to design products that are intuitively useable. This thesis focusses on developing a quantitative approach for evaluating intuitive interaction with the help of image schemas. An image schema is a recurring structure within the human cognitive system that establishes patterns of reasoning with the physical world. Researchers have found this concept to be very useful in capturing human interaction with products; it is also suitable for analysing product features. The approach developed in this thesis measures intuitive interaction based on the degree of match (known in this study as quantification, ‘Q’) between the designer’s intent and the users’ interaction both expressed through image schemas. The value of the proposed approach is in the provision of measurable outputs for evaluating intuitive interaction. The proposed approach is evaluated through an empirical study designed to test the validity of ‘Q’ as a measure of intuitive use expressed through task completion time, errors and cognitive effort. The results reveal that participants with high ‘Q’ value were significantly quicker, made fewer errors, and expended less cognitive effort while completing all subtasks with the three products used in the study. Secondly, the thesis addresses the limitation of other studies in the applicability of the methods used for extracting image schemas. Previous approaches have predominantly relied on the expertise of the researcher conducting the study in extracting the identified image schemas from the utterances of the users. This could introduce subjective bias, thereby reducing the reliability of the method for extracting image schemas. This study addressed this limitation by developing a systematic approach based on a novel algorithm for extracting image schemas from users’ utterances. This enhanced image schema extraction method is based on the use of two ontologies: a lexical ontology and a domainspecific ontology (image schema ontology). The domain-specific ontology was purpose-built for the needs of this study. The independent evaluation study conducted to evaluate the algorithm revealed a substantial strength with an overall k of 0.67 across the 3 products used in the study. Previous studies have predominantly focussed on the cognitive aspect of intuitive use. A limited amount of work has explored the affective aspect of intuition, and integrated it into the evaluation of intuitive use. This study addressed this limitation by developing a novel approach for assessing the affective aspect of the intuitive use of products. This novel approach incorporates the enhanced algorithm for extracting image schemas that was developed for this study. In addition, a sentiment analysis on the affective words linked to the image schemas employed in the task is used as part of the evaluation process. The proposed approach is evaluated through an empirical study based on the sentiments used in describing the image schemas employed for the interaction. The results show that the approach links the image schemas used for the completion of a task to the affective experiences of the users. This has potential to lead to significant improvements in design for intuitive use because it allows experiences to be linked directly to the specific image schemas employed in the design. Overall, the study contributes significantly to the knowledge in this field by validating a new quantitative approach for evaluating intuitive interactions with physical objects. The approaches developed in this study will enable designers to evaluate intuitive usage at different phases of the design process

Exploring figurative language recognition: a comprehensive study of human and machine approaches

Treballs Finals de Grau de Llengües i Literatures Modernes. Facultat de Filologia. Universitat de Barcelona. Curs: 2022-2023. Tutora: Elisabet Comelles Pujadas[eng] Figurative language (FL) plays a significant role in human communication. Understanding and interpreting FL is essential for humans to fully grasp the intended message, appreciate cultural nuances, and engage in effective interaction. For machines, comprehending FL presents a challenge due to its complexity and ambiguity. Enabling machines to understand FL has become increasingly important in sentiment analysis, text classification, and social media monitoring, for instance, benefits from accurately recognizing figurative expressions to capture subtle emotions and extract meaningful insights. Machine translation also requires the ability to accurately convey FL to ensure translations reflect the intended meaning and cultural nuances. Therefore, developing computational methods to enable machines to understand and interpret FL is crucial. By bridging the gap between human and machine understanding of FL, we can enhance communication, improve language-based applications, and unlock new possibilities in human-machine interactions. Keywords: figurative language, NLP, human-machine communication.[cat] El Llenguatge Figuratiu (LF) té un paper important en la comunicació humana. Per entendre completament els missatges, apreciar els matisos culturals i la interacció efectiva, és necessària la capacitat d'interpretar el LF. No obstant això, els ordinadors tenen dificultats per entendre la LF a causa de la seva complexitat i ambigüitat. És crític que els ordinadors siguin capaços de reconèixer el LF, especialment en àrees com l'anàlisi de sentiments, la classificació de textos i la supervisió de les xarxes socials. El reconeixement precís del LF permet capturar emocions i extreure idees semàntiques. La traducció automàtica també requereix una representació precisa del LF per reflectir el significat previst i els matisos culturals. Per tant, és rellevant desenvolupar mètodes computacionals que ajudin els ordinadors a comprendre i interpretar el LF. Fer un pont entre la comprensió humana i màquina del LF pot millorar la comunicació, desenvolupar aplicacions de llenguatge i obrir noves possibilitats per a la interacció home-màquina. Paraules clau: llenguatge figuratiu, processament del llenguatge natural, interacció home-màquina

BlogForever: D3.1 Preservation Strategy Report

This report describes preservation planning approaches and strategies recommended by the BlogForever project as a core component of a weblog repository design. More specifically, we start by discussing why we would want to preserve weblogs in the first place and what it is exactly that we are trying to preserve. We further present a review of past and present work and highlight why current practices in web archiving do not address the needs of weblog preservation adequately. We make three distinctive contributions in this volume: a) we propose transferable practical workflows for applying a combination of established metadata and repository standards in developing a weblog repository, b) we provide an automated approach to identifying significant properties of weblog content that uses the notion of communities and how this affects previous strategies, c) we propose a sustainability plan that draws upon community knowledge through innovative repository design

Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review

Purpose: Sentiment analysis is built from the information provided through text (reviews) to help understand the social sentiment toward their brand, product, or service. The main purpose of this paper is to draw an overview of the topics and the use of the sentiment analysis approach in tourism research. Methods: The study is a bibliometric analysis (VOSviewer), with a systematic and integrative review. The search occurred in March 2021 (Scopus) applying the search terms "sentiment analysis" and "tourism" in the title, abstract, or keywords, resulting in a final sample of 111 papers. Results: This analysis pointed out that China (35) and the United States (24) are the leading countries studying sentiment analysis with tourism. The first paper using sentiment analysis was published in 2012; there is a growing interest in this topic, presenting qualitative and quantitative approaches. The main results present four clusters to understand this subject. Cluster 1 discusses sentiment analysis and its application in tourism research, searching how online reviews can impact decision-making. Cluster 2 examines the resources used to make sentiment analysis, such as social media. Cluster 3 argues about methodological approaches in sentiment analysis and tourism, such as deep learning and sentiment classification, to understand the user-generated content. Cluster 4 highlights questions relating to the internet and tourism. Implications: The use of sentiment analysis in tourism research shows that government and entrepreneurship can draw and enhance communication strategies, reduce cost, and time, and mainly contribute to the decision-making process and understand consumer behavior

Revisiting Urban Dynamics through Social Urban Data:

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities? To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.   After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources. A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics. The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities

Revisiting Urban Dynamics through Social Urban Data

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities?  To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.  After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources.  A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics.  The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities

Enriching companion robots with enhanced reminiscence abilities

In this document I will go on discussing a project conceived by Professor Andrea Giovanni Nuzzolese and Alessandro Russo, both researchers and developers of some of the main aspects of project Mario at CNR Rome. MARIO is a robot, part of a robotics company called KOMPAÏ Robotics that deals with the production and management of Robots who take care of elderly people who suffer from dementia or who still need an aid; more generally speaking, there is talk of weak and lonely people within an organization and / or institutions (nursing homes ...) or in their own homes. There are numerous characteristics of MARIO, which ultimately contribute to all those which are the manufacturing objectives of KOMPAÏ Robotics. My project, or rather my contribution to MARIO, is to look for a specific method which let the robot show a specific set of photos to the user according to the expressions, feelings and emotions, the user will reveal. Example: the robot randomly chooses a marriage photo and the user suddenly start to laugh and to express positive feelings with positive words; the robot will try to understand if it’s a good photo for the user or not, and in the first case will continue to show the same kind of pictures while in the second case, will change completely set of photos to be shown. The pleasure of the subject expressed in relation to a photo must be subject to an index of interest between predefined and specified values that may be to show a certain interest in a picture or the subjects within the image or the situation that surrounds them