The Value of Plurality in 'The Network with a Thousand Entrances'

This contribution reflects on the value of plurality in the ‘network with a thousand entrances’ suggested by McCarty (http://goo.gl/H3HAfs), and others, in association with approaching time-honoured annotative and commentary practices of much-engaged texts. The question is how this approach aligns with tensions, today, surrounding the multiplicity of endeavour associated with modeling practices of annotation by practitioners of the digital humanities. Our work, hence, surveys annotative practice across its reflection in contemporary praxis, from the MIT annotation studio whitepaper (http://goo.gl/8NBdnf) through the work of the Open Annotation Collaboration (http://www.openannotation.org), and manifest in multiple tools facilitating annotation across the web up to and including widespread application in social knowledge creation suites like Wikipedia https://en.wikipedia.org/wiki/Web annotation

Connecting the dots : playful interaction with scientific image data in repositories

Scientific practice is an activity that is data-intensive and widely supported by computerized systems, data repositories included. It is also an activity that is highly creative and, as such, can benefit from a moment of openness, playfulness and exploration. Motivated also by recent developments in the field of Human Computer Interaction regarding play and games, this work investigates playfulness as a desirable attribute of a scientist's interaction with scientific data in repositories. Focus is on data repositories of a specific domain of science, i.e. the life sciences, and of a particular type of data, i.e. image data. Having introduced a new but relevant attribute for interfaces to scientific image repositories, i.e. playfulness, the question we ask is the following: What could playfulness with scientific images amount to and how do we design for it? Via case studies and reviews, we flesh out particular elements of play for exploration and implement artefacts, i.e. interfaces and games, that exemplify instances of playful interaction with image research material in collections.LEI Universiteit LeidenComputer Systems, Imagery and Medi

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used