In this paper, we present a sentiment-annotated Twitter gold standard for the Brexit referendum. The data set consists of 2,000 Twitter messages ("tweets") annotated with information about the sentiment expressed, the strength of the sentiment, and context dependence. This is a valuable resource for social media-based opinion mining in the context of political events. CCS Concepts• Computing methodologies➝Artificial intelligence➝Natural language processing➝Language resources.

Explicit representations of images are useful for linguistic applications related to images. We design a representation based on first-order models that capture the objects present in an image as well as their spatial relations. We take a supervised learning approach to the spatial relation classification problem and study the effects of spatial and lexical information on prediction performance. We find that lexical information is required to accurately predict spatial relations when combined with location information, achieving an F-score of 0.80, compared to a most-frequent-class baseline of 0.62.

No abstract

No abstract

No abstract