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The popularity of social media over the past several years, especially sites such as Twitter, has presented a network for up to the minute information on events across the globe. The information presented on these sites can be extremely helpful in the case of an emergency, however, the vast amount of data to examine and the low adoption of geo-tagging on this site makes it difficult, if not impossible,...
Large online libraries are now common across the Web. However these libraries are usually fragmented and are not semantically connected thereby making their search and access difficult. Also there are no large bibliographic Linked Open Datasets available for use in research and analytics. This paper shows how to create a large, comprehensive, RDF triple store of semantic data about books. The primary...
Inspired by the shift of vocabulary development projects towards repository hosting services such as GitHub, we noticed the lack of ontology-aware editors that can be easily connected to these repositories. This motivated us to build a web client optimized for the communication with external repositories and including specific functionalities to ease the participation in collaborative ontology development...
We present a framework for sentiment analysis on tweets related to news items. Given a set of tweets and news items, our framework classifies tweets as positive or negative and links them to the related news items. For the classification of tweets we use three of the most used machine learning methods, namely Naive Bayes, Complementary Naive Bayes, and Logistic Regression, and for linking tweets to...
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