@conference {168, title = {LEDBoard: Using visual feedback to support text entry with physical keyboards}, booktitle = {14th International Conference on Ubiquitous Computing \& Ambient Intelligence (UCAmI 2022)}, year = {2022}, publisher = {Springer Cham}, organization = {Springer Cham}, address = {Cordoba, Spain}, abstract = {Physical keyboards persist as one of the most common input devices for personal computers. Low familiarity with the keyboard translates to frustration during text entry, as the user constantly shifts their attention between the keyboard and the screen, in order to locate the next key to press, and to inspect input for errors. To decrease the need for attention shifts, we present physical keyboard typists with visual feedback, within their field of view, using an RGB LED strip to indicate spelling errors in different colours. We conducted a user experiment with 36 participants. Users{\textquoteright} performance was evaluated a) without visual feedback, b) showing feedback with a LED strip on the keyboard, and c) showing feedback with a LED strip at the bottom of the screen. We find that our prototype improves corrective action behaviour for slow typists and reduces screen-glancing behaviour for fast typists.}, keywords = {Intelligent keyboards, Physical keyboards, text entry, Text Entry Support, Visual feedback}, doi = {10.1007/978-3-031-21333-5_85}, author = {Komninos, Andreas and Kavvathas, Vassilios and Simou, Ioulia} } @conference {137, title = {Leveraging Social Media Linguistic Features for Bilingual Microblog Sentiment Classification}, booktitle = {10th International Conference on Information, Intelligence, Systems and Applications (IISA{\textquoteright}19)}, year = {2019}, month = {07/2019}, publisher = {IEEE}, organization = {IEEE}, address = {Patras, Greece}, abstract = {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.}, keywords = {emotion recognition, natural language processing, sentiment analysis, social networks}, doi = {10.1109/IISA.2019.8900674}, author = {Tsamis, Konstantinos and Andreas Komninos and John Garofalakis} } @conference {106, title = {A lightweight algorithm for the emotional classification of crowdsourced venue reviews}, booktitle = {21st ACM Panhellenic Conference on Informatics}, year = {2017}, month = {09/2017}, publisher = {ACM}, organization = {ACM}, address = {Larisa, Greece}, abstract = {Finding emotions in text is an area of research with wide- ranging applications. Analysis of sentiment in text can help determine the opinions and affective intent of writers, as well as their a itudes, evaluations and inclinations with respect to various topics. Previous work in sentiment analysis has been done on a variety of text genres, including product and movie reviews, news stories, editorials and opinion articles, or blogs. We describe a lightweight emotion annotation algorithm for identifying emotion category \& intensity in reviews wri en by social media (Foursquare) users. e algorithm is evaluated against human subject performance and is found to compare favourably. is work opens up opportunities for solving the problem of helping user navigate through the plethora of venue reviews in mobile and desktop applications.}, keywords = {Sentiment polarity, social networks, Venue tips}, doi = {10.1145/3139367.3139422}, author = {Panayiotis Kolokythas and Andreas Komninos and Lydia Marini and John Garofalakis} } @conference {67, title = {Location sharing services as sensors for analyzing airports{\textquoteright} traffic}, booktitle = {11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous 2014)}, year = {2014}, month = {12/2014}, abstract = {Location sharing services can be seen as {\textquotedblleft}social sensors{\textquotedblright} that provide data revealing real world interactions. In this paper, we examine the case of foursquare check-ins at airports and we show that these data can be indicative of the passengers{\textquoteright} traffic, while their number is hundreds of times lower than the number of actual traffic observations. }, keywords = {Check-ins, Location sharing services, social networks, Ubiquitous social computing.}, doi = {10.4108/icst.mobiquitous.2014.257878}, author = {John Garofalakis and Ioannis Georgoulas and Andreas Komninos and Periklis Ntentopoulos and Athanasios Plessas} }