1 Modeling Diverse Standpoints in Text Classification: Learning to Be Human by Modeling Human Values

An annotator’s classification of a text not only tells us something about the intent of the text’s author, it also tells us something about the annotator’s standpoint. To understand authorial intent, we can consider all of these diverse standpoints, as well as the extent to which the annotators ’ standpoints affect their perceptions of authorial intent. To model human behavior, it is important to model humans ’ unique standpoints. Human values play an especially important role in determining human behavior and how people perceive the world around them, so any effort to model human behavior and perception can benefit from an effort to understand and model human values. Instead of training humans to obscure their standpoints and act like computers, we should teach computers to have standpoints of their own

Enquiry of Unique Human Values: A Systematic Literature Review

The concept of human values has been in the fields of psychology, philosophy, ethics, social sciences, health, environmental management and business. However, this overabundance of research in different fields resulting in different values, measuring methodologies and instruments, conspicuously showing the lack of agreement on its content and structure. Thus in this study, review is presented on values concepts, its diverse categories and lack of consensus on uniqueness of human values among researchers. The importance and need of such investigation is not only highlighted, but also carried out by performing systematic literature review (SLR) on human, individual or personal values (H-I-P). In particularly, the range of values for H-I-P is identified and enlisted from the literature and convert these explicit, implicit and conceptual duplication to unique values by applying constant comparison and memoing techniques of grounded theory. Finally these unique H-I-P values are grouped and classified, based on common characteristic and existing literature. This values list not only integrates scholars by providing foundation of unique H-I-P values, but also act as a reference list of values contents, for futuristic research. Keywords: Human values, systematic literature review (SLR), unique values, values contents Human or individual or personal (H-I-P) values, grounded theor

Libraries and the Missing Narrative: Practitioner Explorations in the use of Design Psychology and Environmental Autobiography for Library Buildings and Designs

Environmental Autobiography as a research method of Environmental Psychology and Design Psychology informs this study of the meaning and experiences of libraries described by six library-building design practitioners. Participants were guided through an adaptation of Toby Israel’s (2010) Design Psychology Toolbox (hereafter known as the DPT or the “Toolbox”) exercises. The research is intended to expand the practice of designing libraries as places and spaces where social and emotional affordance is supported. Emphasizing the significance of libraries as place and space where people often have rich and even transformative experiences serves to augment use-efficiency and evidence-based space planning. Primary goals of the study included providing an opportunity for library-building and design practitioners to tap into their own environmental autobiographies to explore how experience creates meaning in the environments of our lives, and to explore how personal narratives in the form of library stories hold rich information about place and space. As part of this research, participants were encouraged to consider which aspects of the DPT exercises they might incorporate into future client intake exchanges and explorations for proposed library-building programs. This dissertation describes a mixed method approach inherent in environmental autobiography research where both in-depth interviewing as well as sketching and mapping are employed as participants recall their past, explore their present, and imagine their future in describing the significance of libraries over their life course

Formalization and modeling of human values for recipient sentiment prediction

Sentiment analysis is viewed generally as a text classification problem involving the prediction of the semantic orientation of a text. Much of the analysis has focused on the sentiment expressed in the sentence or by the writer but not the sentiment of the recipient. For example, the sentence “Housing costs have dropped significantly” might be assigned a negative classification by a sentiment analysis model, however humans from different works of life might express different sentiments. A landlord will likely express a negative sentiment while a renter might express a positive sentiment. Therefore, traditional sentiment analysis methods fail to capture the human centric aspects that motivate diverse sentiments. Additionally, attempts at predicting recipient sentiment have involved considerable human effort in the form of content analysis and empirical surveys, making the process expensive and time-consuming. Thus, the aim of this research is to develop a method of recipient sentiment analysis that is devoid of human input in the form of annotations or empirical surveys. The approach taken in this research involves applying a model of human values towards recipient sentiment prediction. The justification for this approach is based on the well-established principle that values influence human behaviour of which sentiment is a form. Therefore, if a persons’ values can be modelled quantitatively, when presented with some text, in theory the sentiment of the recipient can be predicted. This research proposes that the application of values in developing sentences is a generative process, that can be represented as a language model. A mechanism called Feature Switching (FS) that enables the determination of recipient’s sentiment from the value language model is also discussed. The resulting sentiment prediction model has an accuracy in the range of 72.2%-72.5% which is in and about the range of performance of existing systems which make use of content analysis and human annotated data