Aspect-based sentiment prediction is a specific area of sentiment analysis that models the sentiment of a text excerpt as a multi-dimensional quantity pertaining to various interpretations, rather than a scalar one, that admits a single explanation. Extending earlier work, the said task is examined as a part of a unified architecture that collects, analyzes and stores documents from various online sources, including blogs & social network posts. The obtained data are processed at various levels; initially, a hybrid, attention-based bi-directional long short-term memory network, coupled with convolutional layers, is used to extract the textual features of the document. Following, an additional number of document metadata are also examined, such as the number of repetitions, the existence, type and frequency of emoji ideograms and, especially, the presence of keywords, assigned either manually (e.g. in the form of hashtags) or automatically. All of the aforementioned features are subsequently provided as input to a fully-connected, multi-layered, feed-forward artificial neural network that performs the final prediction task. The overall approach is tested on a large corpus of documents, with encouraging results.

Package recommendation systems have gained in popularity especially in the tourism domain, where they propose combinations of different types of attractions that can be visited by someone during a city tour. These systems can also be applied in suggesting home entertainment, proper nutrition or academic courses. Such systems must optimize multiple user criteria in tandem, such as preference score, package cost or duration. This work proposes a flexible framework for recommending packages that best fit users' preferences while satisfying several constraints on the set of the valid packages. This is achieved by modeling the relation between the items and the categories these items belong to, aiming at recommending to each user the top-k packages that cover their preferred categories and the restriction of a maximum package cost. Our contribution includes an optimal and a greedy algorithm, that both outperform a state-of-the-art system and a popularity-based baseline solution. The novelty of the optimal algorithm is that it combines the collaborative filtering predictions with a graph-based model to produce package recommendations. The problem is expressed through a minimum cost flow network and is solved by integer linear programming. The greedy algorithm has a low computational complexity and provides recommendations which are close to the optimal one. An extensive evaluation of the proposed framework has been carried out on six popular recommendation datasets. The results obtained using a set of widely accepted metrics show promising performance. Finally, the formulation of the problem for specific domains has also been addressed.

Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, recommendation systems and personalization techniques have created a smart research field, augmenting cultural heritage visitor’s experience. In this work, a novel, hybrid recommender system for cultural places is proposed, that combines user preference with cultural tourist typologies. Starting with the McKercher typology as a user classification research base, which extracts five categories of heritage tourists out of two variables (cultural centrality and depth of user experience) and using a questionnaire, an enriched cultural tourist typology is developed, where three additional variables governing cultural visitor types are also proposed (frequency of visits, visiting knowledge and duration of the visit). The extracted categories per user are fused in a robust collaborative filtering, matrix factorization-based recommendation algorithm as extra user features. The obtained results on reference data collected from eight cities exhibit an improvement in system performance, thereby indicating the robustness of the presented approach.

Sentiment analysis is a vigorous research area, with many application domains. In this work, aspect-based sentiment prediction is examined as a component of a larger architecture that crawls, indexes and stores documents from a wide variety of online sources, including the most popular social networks. The textual part of the collected information is processed by a hybrid bi-directional long short-term memory architecture, coupled with convolutional layers along with an attention mechanism. The extracted textual features are then combined with other characteristics, such as the number of repetitions, the type and frequency of emoji ideograms in a fully-connected, feed-forward artificial neural network that performs the final prediction task. The obtained results, especially for the negative sentiment class, which is of particular importance in certain cases, are encouraging, underlying the robustness of the proposed approach.

Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.

A critical issue for the safe operation of nuclear power plants is to quickly and accurately detect possible anomalies and perturbations in the reactor. Defects in operation are principally identified through changes in the neutron flux, as captured by detectors placed at various points inside and outside of the core. While wavelet-based analysis of the measured signals has been thoroughly used for anomaly detection, it has yet to be coupled with deep learning approaches. To this end, this work presents a novel technique for anomaly detection on nuclear reactor signals through the combined use of wavelet-based analysis and convolutional neural networks. In essence, the wavelet transform is applied to the signals and the corresponding scaleograms are produced, which are subsequently used to train a convolutional neural network that detects possible perturbations in the reactor core. The overall methodology is experimentally validated on a set of simulated nuclear reactor signals generated by a well-established relevant tool. The obtained results indicate that the trained network achieves high levels of accuracy in failure detection, while at the same time being robust to noise.