Nowadays, stress has become a growing problem for society due to its high impact on individuals but also on health care systems and companies. In order to overcome this problem, early detection of stress is a key factor. Previous studies have shown the effectiveness of text analysis in the detection of sentiment, emotion, and mental illness. However, existing solutions for stress detection from text are focused on a specific corpus. There is still a lack of well-validated methods that provide good results in different datasets. We aim to advance state of the art by proposing a method to detect stress in textual data and evaluating it using multiple public English datasets. The proposed approach combines lexicon-based features with distributional representations to enhance classification performance. To help organize features for stress detection in text, we propose a lexicon-based feature framework that exploits affective, syntactic, social, and topic-related features. Also, three different word embedding techniques are studied for exploiting distributional representation. Our approach has been implemented with three machine learning models that have been evaluated in terms of performance through several experiments. This evaluation has been conducted using three public English datasets and provides a baseline for other researchers. The obtained results identify the combination of FastText embeddings with a selection of lexicon-based features as the best-performing model, achieving F-scores above 80%.

Occupational stress has a significant adverse effect on workers’ well-being, productivity, and performance and is becoming a major concern for both individual companies and the overall economy. To reduce negative consequences, early detection of stress is a key factor. In response several stress prediction methods have been proposed, whose primary aim is to analyse physiological and behavioural data. However, evidence suggests that solutions based on physiological and behavioural data alone might be challenging when implemented in real-world settings. These solutions are sensitive to data problems arising from losses in signal quality or alterations in body responses, which are common in everyday activities. The contagious nature of stress and its sensitivity to the surroundings can be used to improve these methods. In this study, we sought to investigate automatic stress prediction using both surrounding stress data, which we define as close colleagues’ stress levels and the stress level history of the individuals. We introduce a real-life, unconstrained study conducted with 30 workers monitored over 8 weeks. Furthermore, we propose a method to investigate the effect of stress levels of close colleagues on the prediction of an individual’s stress levels. Our method is also validated on an external, independent dataset. Our results show that surrounding stress can be used to improve stress prediction in the workplace, where we achieve 80% of F-score in predicting individuals’ stress levels from the surrounding stress data in a multiclass stress classification.

The Internet has become an effective tool for terrorist and radical groups to spread their propaganda. One of the current problems is to detect these radical messages in order to block them or promote counter-narratives.In this work, we propose the use of stylometric methods for characterizing radical messages. We have used a machine learning approach to classify radical texts based on a corpus of news from radical sources such as the so-called ISIS online magazines Dabiq and Rumiyah, as well as news from general newspapers. The results show that stylometric features are effective for radical text classification.

Museums have become through time the cultural conscience of nations by preserving cultural heritage. Although cultural heritage provides a valuable form of learning about mankind, the display of exhibits might not pique visitors' interest. In order to provide engaging and appealing experiences, museums have integrated a number of technologies in their environments. Particularly, there are promising results with the emerging Internet of Things (IoT) technology, enabling the implementation of smart objects to grant exhibits advanced capabilities. Gamification techniques are also commonly used in cultural heritage scenarios to further draw visitors' attention. On this sense, several museums offer for example interactive question-based games. However, these questions must be periodically renewed and such task is time-consuming given the lack of automation of this procedure.To address these challenges, this paper proposes a low-maintenance gamified smart object platform to automate the creation of questions about exhibits by exploiting semantic web technologies. The main contributions of this paper are: (i) the design of a gamified smart object platform for museums based on a trivia quiz app; (ii) the automation of question generation schemes for the proposed game; and (iii) the development of a prototype of the proposed platform in a real-life scenario.

Lexical resources are widely popular in the field of Sentiment Analysis, as they represent a resource that directly encodes sentimental knowledge. Usually sentiment lexica are used for polarity estimation through the matching of words contained in a text and their associated lexicon sentiment polarities. Nevertheless, such resources have limitations in vocabulary coverage and domain adaptation. Besides, many recent techniques exploit the concept of distributed semantics, normally through word embeddings. In this work, a semantic similarity metric is computed between text words and lexica vocabulary. Using this metric, this paper proposes a sentiment classification model that uses the semantic similarity measure in combination with embedding representations. In order to assess the effectiveness of this model, we perform an extensive evaluation. Experiments show that the proposed method can improve Sentiment Analysis performance over a strong baseline, being this improvement statistically significant. Finally, some characteristics of the proposed technique are studied, showing that the selection of lexicon words has an effect in cross-dataset performance.

With the increasing popularity of large scale Knowledge Graph (KG)s, many applications such as semantic analysis, search and question answering need to link entity mentions in texts to entities in KGs. Because of the polysemy problem in natural language, entity disambiguation is thus a key problem in current research. Existing disambiguation methods have considered entity prominence, context similarity and entity-entity relatedness to discriminate ambiguous entities, which are mainly working on document or paragraph level texts containing rich contextual information, and based on lexical matching for computing context similarity. When meeting short texts containing limited contextual information, such as web queries, questions and tweets, those conventional disambiguation methods are not good at handling single entity mention and measuring context similarity. In order to enhance the performance of disambiguation methods based on context similarity with such short texts, we propose SCSNED method for disambiguation based on semantic similarity between contextual words and informative words of entities in KGs. Specially, we exploit the effectiveness of both knowledge-based and corpus-based semantic similarity methods for entity disambiguation with SCSNED. Moreover, we propose a Category2Vec embedding model based on joint learning of word and category embedding, in order to compute word-category similarity for entity disambiguation. We show the effectiveness of these proposed methods with illustrative examples, and evaluate their effectiveness in a comparative experiment for entity disambiguation in real world web queries, questions and tweets. The experimental results have identified the effectiveness of different semantic similarity methods, and demonstrated the improvement of semantic similarity methods in SCSNED and Category2Vec over the conventional context similarity baseline. We further compare the proposed approaches with the state of the art entity disambiguation systems and show the performances of the proposed approaches are among the best performing systems. In addition, one important feature of the proposed approaches using semantic similarity, is the potential application on any existing KGs since they mainly use common features of entity descriptions and categories. Another contribution of the paper is an updated survey on background of entity disambiguation in KGs and semantic similarity methods.

The application of natural language to improve the interaction of human users with information systems is a growing trend in the recent years. Advances in cognitive computing enable a new way of interaction that accelerates insight from existing information sources. In this paper, we propose a modular cognitive agent architecture for question answering featuring social dialogue improved for a specific knowledge domain. The proposed system has been implemented as a personal agent to assist students learning Java programming language. The developed prototype has been evaluated to analyze how users perceive the interaction with the system. We claim that including social dialogue in QA systems increases users satisfaction and makes them easily engage with the system. Finally, we present the evaluation results that support our hypotheses.

Recently, there is an increasing tendency to embed functionalities for recognizing emotions from user-generated media content in automated systems such as call-centre operations, recommendations, and assistive technologies, providing richer and more informative user and content profiles. However, to date, adding these functionalities was a tedious, costly, and time-consuming effort, requiring identification and integration of diverse tools with diverse interfaces as required by the use case at hand. The MixedEmotions Toolbox leverages the need for such functionalities by providing tools for text, audio, video, and linked data processing within an easily integrable plug-and-play platform. These functionalities include: 1) for text processing: emotion and sentiment recognition; 2) for audio processing: emotion, age, and gender recognition; 3) for video processing: face detection and tracking, emotion recognition, facial landmark localization, head pose estimation, face alignment, and body pose estimation; and 4) for linked data: knowledge graph integration. Moreover, the MixedEmotions Toolbox is open-source and free. In this paper, we present this toolbox in the context of the existing landscape, and provide a range of detailed benchmarks on standard test-beds showing its state-of-the-art performance. Furthermore, three real-world use cases show its effectiveness, namely, emotion-driven smart TV, call center monitoring, and brand reputation analysis.

Deep learning techniques for Sentiment Analysis have become very popular. They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods). Traditional surface approaches are based on complex manually extracted features, and this extraction process is a fundamental question in feature driven methods. These long-established approaches can yield strong baselines, and their predictive capabilities can be used in conjunction with the arising deep learning methods. In this paper we seek to improve the performance of deep learning techniques integrating them with traditional surface approaches based on manually extracted features. The contributions of this paper are sixfold. First, we develop a deep learning based sentiment classifier using a word embeddings model and a linear machine learning algorithm. This classifier serves as a baseline to compare to subsequent results. Second, we propose two ensemble techniques which aggregate our baseline classifier with other surface classifiers widely used in Sentiment Analysis. Third, we also propose two models for combining both surface and deep features to merge information from several sources. Fourth, we introduce a taxonomy for classifying the different models found in the literature, as well as the ones we propose. Fifth, we conduct several experiments to compare the performance of these models with the deep learning baseline. For this, we use seven public datasets that were extracted from the microblogging and movie reviews domain. Finally, as a result, a statistical study confirms that the performance of these proposed models surpasses that of our original baseline on F1-Score.

This paper presents a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic network between concepts (e.g., path length and depth), or only on the Information Content (IC) of concepts. We propose a semantic similarity method, namely wpath, to combine these two approaches, using IC to weight the shortest path length between concepts. Conventional corpus-based IC is computed from the distributions of concepts over textual corpus, which is required to prepare a domain corpus containing annotated concepts and has high computational cost. As instances are already extracted from textual corpus and annotated by concepts in KGs, graph-based IC is proposed to compute IC based on the distributions of concepts over instances. Through experiments performed on well known word similarity datasets, we show that the wpath semantic similarity method has produced a statistically significant improvement over other semantic similarity methods. Moreover, in a real category classification evaluation, the wpath method has shown the best performance in terms of accuracy and F score.

The growing popularity of public APIs and technologies such as web hooks is changing online services drastically. It is easier now than ever to interconnect services and access them as a third party. The next logical step is to use intelligent agents to provide a better user experience across services, connecting services with smart automatic behaviors or actions. In other words, it is time to start using agents in the so-called Evented Web. For this to happen, agent platforms need to seamlessly integrate external sources such as web services. As a solution, this paper introduces an event-based architecture for agent systems. This architecture has been designed in accordance with the new tendencies in web programming and with a Linked Data approach. The use of Linked Data and a specific vocabulary for events allows a smarter and more complex use of events. Two use cases have been implemented to illustrate the validity and usefulness of the architecture.

Sentiment and emotion analysis technologies have quickly gained momentum in industry and academia. This popularity has spawned a myriad of service and tools. Due to the lack of common interfaces and models, each of these services imposes specific interfaces and representation models. Heterogeneity makes it costly to integrate different services, evaluate them or switch between them. This work aims to remedy heterogeneity by providing an extensible framework and an API aligned with the NIF service specification. It also includes a reference implementation, a first step towards a successful and cost-effective adoption. The specific contributions in this paper are: (i) the Senpy framework, (ii) an architecture for the framework that follows a plug-in approach, (iii) a reference open source implementation of the architecture, (iv) the use and validation of the framework and architecture in a big data sentiment analysis European project. Our aim is to foster the development of a new generation of emotion aware services by isolating the development of new algorithms from the representation of results and the deployment of services.

There exists a growing trend in using NLIs (Natural Language Interfaces) that ranges from research to commercial products. Conversational agents beneath these interfaces have become more sophisticated, being able to either perform a task in behalf of the user or give a precise response to a question as Question Answering systems do. When combining Conversational Agents with QA capabilities the maintenance cost exponentially increases. In this paper we propose a hybrid architecture for a Question Answering system that features social dialog. We claim that including social dialog in QA systems increases users satisfaction and makes them easily engage with the system. Finally, we present an evaluation that supports these hypotheses.

One of the challenges facing the current web is the efficient use of all the available information. The Web 2.0 phenomenon has favored the creation of contents by average users, and thus the amount of information that can be found for diverse topics has grown exponentially in the last years. Initiatives such as linked data are helping to build the Semantic Web, in which a set of standards are proposed for the exchange of data among heterogeneous systems. However, these standards are sometimes not used, and there are still plenty of websites that require naive techniques to discover their contents and services. This paper proposes an integrated framework for content and service discovery and extraction. The framework is divided into several layers where the discovery of contents and services is made in a representational stateless transfer system such as the web. It employs several web mining techniques as well as feature-oriented modeling for the discovery of cross-cutting features in web resources. The framework is used in a scenario of electronic newspapers. An intelligent agent crawls the web for related news, and uses services and visits links automatically according to its goal. This scenario illustrates how the discovery is made at different levels and how the use of semantics helps implement an agent that performs high-level tasks.

Idea Management Systems are an implementation of open innovation notion in the Web environment with the use of crowdsourcing techniques. In this area, one of the popular methods for coping with large amounts of data is duplicate detection. With our research, we answer a question if there is room to

The dHDL language has been defined to improve hardware design productivity. This is achieved through the definition of a better reuse interface (including parameters, attributes and macroports) and the creation of control structures that help the designer in the hardware generation process.

The evolution of communications networks to Next Generation Networks (NGN) has encouraged the development of new services. Nowadays, several technologies are being integrated into telecommunications services in order to provide new functionalities, resulting in what are known as converged services. The objective is to adapt the behavior of the services to the necessities of different users, generating customized services.

Nowadays, web development is one of the main activities in software development, with a wide array of tools that make it difficult for developers to deal with its heterogeneity. The appearance of Ruby on Rails has brought a new paradigm to current web development frameworks, and has shown how an agile web development framework can simplify the development process, with a considerable productivity increment. There are several Java-based alternatives to Ruby on Rails, such as Grails, Roma, Trails, JBoss Seam or Sails, with different approaches to the reuse of previous Java frameworks and technologies. This paper proposes a comparison model for agile web frameworks to facilitate developers the selection of the most suitable for each case. This paper reviews the state of the art of agile web frameworks. Afterwards, a comparison model based on a set of evaluation criteria is defined for web framework evaluation. Finally, the model is applied to the most popular web frameworks.

This paper presents SHAPES, a tool for hardware-software partitioning. It is based on two main paradigms: the implementation of the partitioning tool by means of an expert system, and the use of fuzzy logic to model the parameters involved in the process.