In this paper we study the connection between sentiment of images expressed in metadata and their visual content in the social photo sharing environment Flickr. To this end, we consider the bag-of-visual words representation as well as the color distribution of images, and make use of the SentiWordNet thesaurus to extract numerical values for their sentiment from accompanying textual metadata. We then perform a discriminative feature analysis based on information theoretic methods, and apply machine learning techniques to predict the sentiment of images. Our largescale empirical study on a set of over half a million Flickr images shows a considerable correlation between sentiment and visual features, and promising results towards estimating the polarity of sentiment in images.

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More and more applications use the RDF framework as their data model and RDF stores to index and retrieve their data. Many of these applications require both structured queries as well as fulltext search. SPARQL addresses the first requirement in a standardized way, while fulltext search is provided by store-specific implementations. RDF benchmarks enable developers to compare structured query performance of different stores, but for fulltext search on RDF data no such benchmarks and comparisons exist so far. In this paper, we extend the LUBM benchmark with synthetic scalable fulltext data and corresponding queries for fulltext-related query performance evaluation. Based on the extended benchmark, we provide a detailed comparison of fulltext search features and performance of the most widely used RDF stores. Results show interesting RDF store insights for basic fulltext queries (classic IR queries) as well as hybrid queries (structured and fulltext queries). Our results are not only valuable for selecting the right RDF store for specific applications, but also reveal the need for performance improvements for certain kinds of queries.

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