The era of big data has, among others, three characteristics: the huge amounts of data created every day and in every form by everyday people, artificial intelligence tools to mine information from those data and effective algorithms that allow this data mining in real or close to real time. On the other hand, opinion mining in social media is nowadays an important parameter of social media marketing. Digital media giants such as Google and Facebook developed and employed their own tools for that purpose. These tools are based on publicly available software libraries and tools such as Word2Vec (or Doc2Vec) and fasttext, which emphasize topic modeling and extract low-level features using deep learning approaches. So far, researchers have focused their efforts on opinion mining and especially on sentiment analysis of tweets. This trend reflects the availability of the Twitter API that simplifies automatic data (tweet) collection and testing of the proposed algorithms in real situations. However, if we are really interested in realistic opinion mining we should consider mining opinions from social media platforms such as Facebook and Instagram, which are far more popular among everyday people. The basic purpose of this paper is to compare various kinds of low-level features, including those extracted through deep learning, as in fasttext and Doc2Vec, and keywords suggested by the crowd, called crowd lexicon herein, through a crowdsourcing platform. The application target is sentiment analysis of tweets and Facebook comments on commercial products. We also compare several machine learning methods for the creation of sentiment analysis models and conclude that, even in the era of big data, allowing people to annotate (a small portion of) data would allow effective artificial intelligence tools to be developed using the learning by example paradigm.

Voting advice applications (VAAs) are online tools that have become increasingly popular and purportedly aid users in deciding which party/candidate to vote for during an election. In this paper we present an innovation to current VAA design which is based on the introduction of a social network element. We refer to this new type of online tool as a social voting advice application (SVAA). SVAAs extend VAAs by providing (a) community-based recommendations, (b) comparison of users' political opinions, and (c) a channel of user communication. In addition, SVAAs enriched with data mining modules, can operate as citizen sensors recording the sentiment of the electorate on issues and candidates. Drawing on VAA datasets generated by the Preference Matcher research consortium, we evaluate the results of the first VAA-Choose4Greece-which incorporated social voting features and was launched during the landmark Greek national elections of 2012. We demonstrate how an SVAA can provide community based features and, at the same time, serve as a citizen sensor. Evaluation of the proposed techniques is realized on a series of datasets collected from various VAAs, including Choose4Greece. The collection is made available online in order to promote research in the field.

This paper proposes a novel, intelligent medium access control mechanism for vehicular ad hoc networks. In particular, the minimum and maximum contention window parameters are dynamically tuned based on network observations, concerning all applications' access categories. The simple, generic, and efficient nonlinear control law built, is based on fuzzy logic control and although we focus on vehicle-to-infrastructure (V2I) communication, the proposed scheme can be easily adapted for vehicle-to-vehicle (V2V) communication. We demonstrate, via simulative evaluation of dense and dynamic conditions, that the proposed scheme offers enhanced differentiation among different applications' access categories; therefore, offering sufficient Quality of Service (QoS) in terms of throughput performance, in contrast with the IEEE 802.11p standard MAC protocol we compared against.

Advances in computing and networking technologies have revolutionised the way people communicate using interactive real-time applications such as instant messaging and audio and videoconferencing. This category of applications is nowadays readily available to users in terrestrial areas with high speed Internet connectivity. The aim of this work is to investigate the provision of IP-based network connectivity onboard sailing vessels in order to support real-time communication applications. More precisely, instead of the expensive and high-latency satellite communications solutions widely deployed in the maritime industry, we propose the use of long-range wireless networking technologies for creating a vessel-to-shore mesh network that can be used to form an infrastructure for the provision of telephony services based on Voice-over-IP (VoIP).

This paper proposes a novel, adaptive medium access control mechanism for vehicular ad hoc networks. A simple, effective, and efficient nonlinear control law is built, based on fuzzy logic control principles, which can be easily adopted in different network environments (e.g. vehicle-to-vehicle (V2V) communication and vehicle-to-infrastructure (V2I) communication). We demonstrate, via simulative evaluation, that the proposed fuzzy control methodology offers inherent robustness with effective control of the system under dense and dynamic conditions, without the need to (re)tune any parameters. The proposed approach offers distinct differentiation among differently prioritized traffic types, thus providing adequate Quality of Service (QoS) in terms of throughput performance, in contrast with the IEEE 802.11p MAC protocol we compared against.