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Most previous Sentiment Analysis (SA) work has focused on English with considerable success. In this work, we focus on studying SA in Arabic, as a less-resourced language. SA in Arabic has been previously addressed in the literature, but has targeted text genres of more formal/edited domains (e.g. news-wire) and domains containing longer text instances, i.e. with more contextual information (e.g. reviews). That is, less work has focused on SA in Arabic for a noisy and short-length text genre, like micro-blogs. In addition, the time-changing nature of streaming data (e.g. the Twitter stream) has not been considered in previous work, as SA systems were mainly developed and evaluated on small test-sets that are subsets of the original data-set used for training. This work reports on a wide set of investigations for SA in Arabic tweets, systematically comparing two existing approaches that have been shown to be successful in English. Unlike previous work, we benchmark the trained models against an independent test-set of >3.5k instances collected at different points in time to account for topic-shifts issues in the Twitter stream. Despite the challenging noisy medium of Twitter and the mixed use of Dialectal and Standard forms of Arabic, we show that our SA systems are able to attain performance scores on Arabic tweets that are comparable to the state-of-the-art SA systems for English tweets.

This paper describes the design of a major development and research project within the UK's Teaching and Learning Research Programme. It outlines the project's aims to enhance teaching and learning in schools through innovative practice at classroom and ...

I also want to thank the superintendent George Smith and the technicians Richard Kinsella and Cameron Smith who were very helpful during my time as PhD. Their experience made it possible to find fast, cheap and available solutions for daily problems and they have made big contribution to finishing all experiments within time. I am also very thankful to all secretaries at HWU who always had time to help through the administrative processes. I am very much thankful to my wife Conny Hien for being always there for me and supporting me in daily life decisions. She gave up her job in Germany and had to find new opportunities in Edinburgh to support me in doing a PhD at Heriot Watt University. Last but not least I want to thank my mother Marianne Hien, my father Josef Hien and my brother Stephan Hien for supporting my decision to go abroad.