Social media and microblogs have become an integral part of everyday life. People use microblogs to communicate with each other, express their opinion about a wide range of topics and inform themselves about issues they are interested in. The increasing volume of information generated in microblogs has led to the need of automatically determining the sentiment expressed in microblog comments. Researchers have worked in systematically analyzing microblog comments in order to identify the sentiment expressed in them. Most work in sentiment analysis of microblog comments has been focused on comments written in the English language, whereas fewer efforts have been made in predicting the sentiment of Greek microblog comments. In this paper, we propose a lexicon-based sentiment analysis algorithm for the sentiment classification of both Greek and English microblog comments. The proposed method uses a unified approach for determining the sentiment of comments written in both languages and incorporates techniques that exploit the distinctive features of the language used in microblogs in order to accurately predict the sentiment expressed in microblog comments. Our approach achieves promising results for the sentiment classification of microblog comments into positive, negative or neutral.

Tsamis, K., Komninos A., & Garofalakis J. (In Press).  Leveraging Social Media Linguistic Features for Bilingual Microblog Sentiment Classification. 10th International Conference on Information, Intelligence, Systems and Applications (IISA'19). Patras, Greece, IEEE.

Predicting the footfall in a new brick-and-mortar shop (and thus, its prosperity), is a problem of strategic importance in business. Few previous attempts have been made to address this problem in the context of big data analytics in smart cities, proposing the use of social network check-ins as a proxy for business popularity, concentrating however only on singular business types. Adding to the existing literature, we mine a large dataset of high temporal granularity check-in data for two medium-sized cities in Southern and Northern Europe, with the aim to predict the evolution of check-ins of new businesses of any type, from the moment that they appear in a social network. We propose and analyze the performance of three algorithms for the dynamic identification of suitable neighbouring businesses, whose data can be used to predict the evolution of a new business. Our SmartGrid algorithm reaches a performance of being able to accurately predict the evolution of 86% of new businesses.

Papadimitriou, G., Komninos A., & Garofalakis J. (In Press).  Supporting Retail Business in Smart Cities using Urban Social Data Mining. 15th International Conference on Intelligent Environments (IntEnv'19). Rabat, Morocco, IEEE.

In smart spaces with connected smart lighting, there is an opportunity to deliver smartphone notifications using peripheral light, along with using standard smartphone modalities such as sound, vibration and LEDs, in order to help a user perceive them without constantly monitoring their mobile device. In this paper, we examine the effectiveness of on-device and extra-device modalities through smart lighting. We address a gap in literature by establishing a foundation that explains the role of modalities with which a notification is delivered on a mobile device. For this purpose, we conducted two ecologically valid and carefully designed experiments in a controlled environment that simulates multitasking in a smart home environment, and demonstrate that modality preferences are dependent on the environment context, by analysing subjective user data through a machine learning approach. We derive a set of guidelines for choosing notification modalities and set future research directions.

Komninos, A., Besharat J., Stefanis V., Gogoulou G., & Garofalakis J. (2019).  Assessing the Perceptibility of Smartphone Notifications in Smart Lighting Spaces. Journal of Ambient Intelligence and Smart Environments. 11(3), 277-297., IOS Press. DOI:10.3233/AIS-190525