2 research outputs found
Machine Learning Approach to Improve Data Connectivity in Text-based Personality Prediction using Multiple Data Sources Mapping
This paper considers the task of personality prediction using social media text data. Personality datasets with conventional personality labels are few, and collecting them is challenging due to privacy concerns and the high expense of hiring expert psychologists to label them. Pertaining to a smaller number of labelled samples available, existing studies usually adds a sentiment, statistical NLP features to the text data to improve the accuracy of the personality detection model. To overcome these concerns, this research proposes a new methodology to generate a large amount of labelled data that can be used by deep learning algorithms. The model has three components: general data representation, data mapping and classification. The model applies Personality correlation descriptors to incorporate correlation information and further use this information in generating dataset mapping algorithm. Experimental results clearly demonstrate that the proposed method beats strong baselines across a variety of evaluation metrics. The results had the highest accuracy of 86.24% and 0.915 F1 measure score on the combined MBTI and Essays dataset. Moreover, the new dataset constructed contains 3,84,089 labelled samples on the combined dataset and can be further considered for personality prediction using the famous Five Factor Model thereby alleviating the problem of limited labelled samples for the purpose of personality detection
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Leveraging digital forensics and data exploration to understand the creative work of a filmmaker: a case study of Stephen Dwoskin’s digital archive
This paper aims to establish digital forensics and data exploration as a methodology for supporting archival practice and research into a filmmaker's creative processes. We approach this by exploring the digital legacy hard drives of the late artist Stephen Dwoskin (1939-2012), who is recognised as an influential filmmaker at the forefront of the shift from analogue to digital film production. The research findings of this case study show that digital forensics is effective in extracting a timeline of hard drive activities, data that can be explored to reveal clues about the artist’s personal/professional history, stages of creative processes, and technical environment. The paper further demonstrates how this is related to current thinking around user-centred archival workflow and understanding of creative processes. The broader impact of the work for advancing digital archiving and research into creative processes is highlighted, concluding with a discussion of how, going forward, the approach can be coupled with deeper content analysis to reveal what influences editing choices taking place over time