Business Ontology for Evaluating Corporate Social Responsibility

This paper presents a software solution that is developed to automatically classify companies by taking into account their level of social responsibility. The application is based on ontologies and on intelligent agents. In order to obtain the data needed to evaluate companies, we developed a web crawling module that analyzes the company’s website and the documents that are available online such as social responsibility report, mission statement, employment structure, etc. Based on a predefined CSR ontology, the web crawling module extracts the terms that are linked to corporate social responsibility. By taking into account the extracted qualitative data, an intelligent agent, previously trained on a set of companies, computes the qualitative values, which are then included in the classification model based on neural networks. The proposed ontology takes into consideration the guidelines proposed by the “ISO 26000 Standard for Social Responsibility”. Having this model, and being aware of the positive relationship between Corporate Social Responsibility and financial performance, an overall perspective on each company’s activity can be configured, this being useful not only to the company’s creditors, auditors, stockholders, but also to its consumers.corporate social responsibility, ISO 26000 Standard for Social Responsibility, ontology, web crawling, intelligent agent, corporate performance, POS tagging, opinion mining, sentiment analysis

Discovering New Sentiments from the Social Web

A persistent challenge in Complex Systems (CS) research is the phenomenological reconstruction of systems from raw data. In order to face the problem, the use of sound features to reason on the system from data processing is a key step. In the specific case of complex societal systems, sentiment analysis allows to mirror (part of) the affective dimension. However it is not reasonable to think that individual sentiment categorization can encompass the new affective phenomena in digital social networks. The present papers addresses the problem of isolating sentiment concepts which emerge in social networks. In an analogy to Artificial Intelligent Singularity, we propose the study and analysis of these new complex sentiment structures and how they are similar to or diverge from classic conceptual structures associated to sentiment lexicons. The conjecture is that it is highly probable that hypercomplex sentiment structures -not explained with human categorizations- emerge from high dynamic social information networks. Roughly speaking, new sentiment can emerge from the new global nervous systems as it occurs in humans

Do narcissism and emotional intelligence win us friends? Modeling dynamics of peer popularity using inferential network analysis

This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study we examined the dynamics of popularity in 15 peer groups in two waves (N=273).We measured narcissism, ability EI, explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and three months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting for self-organizing network forces. People high in narcissism were popular, but increased less in popularity over time than people lower in narcissism. In contrast, emotionally intelligent people increased more in popularity over time than less emotionally intelligent people. The effects held when we controlled for explicit and implicit self-esteem. These results suggest that narcissism is rather disadvantageous and that EI is rather advantageous for long-term popularity

Editorial for Vol.29, No.3

The four papers that make up the September 2021 issue (Vol. 29, No. 3) of CIT. Journal of Computing and Information Technology cover the areas of countering malicious activities in social networks, predictions in intelligent agriculture, use of artificial intelligence in the judiciary, and modeling of intelligent logistics

Social Networking Individual vs. Crowd Behavior (Connected Intelligence)

The study of Human behavior is much more complicated in various situations, especially on the spectrum of Social Networks. The study of individual behavior cannot be replicated for a group/crowd behavior which can have many social and behavioral dimensions. In the connected world where intelligence is shared among individuals and groups, there exists another kind of complexity which needs to be examined.The complexity of human behaviors as an individual or as a group on the social networks is much more versatile and erratic. The research work studies and analyzes these behaviors in a connected networked intelligent environment and as to how these behaviors are reflected towards Connected Intelligence. Consequently it defines how they can affect the intelligent analytical outcomes. Finally it comes up with a generic model which can be applied in any setup

Computational intelligent methods for trusting in social networks

104 p.This Thesis covers three research lines of Social Networks. The first proposed reseach line is related with Trust. Different ways of feature extraction are proposed for Trust Prediction comparing results with classic methods. The problem of bad balanced datasets is covered in this work. The second proposed reseach line is related with Recommendation Systems. Two experiments are proposed in this work. The first experiment is about recipe generation with a bread machine. The second experiment is about product generation based on rating given by users. The third research line is related with Influence Maximization. In this work a new heuristic method is proposed to give the minimal set of nodes that maximizes the influence of the network

Deep Learning Architectures for Novel Problems

With convolutional neural networks revolutionizing the computer vision field it is important to extend the capabilities of neural-based systems to dynamic and unrestricted data like graphs. Doing so not only expands the applications of such systems, but also provide more insight into improvements to neural-based systems. Currently most implementations of graph neural networks are based on vertex filtering on fixed adjacency matrices. Although important for a lot of applications, vertex filtering restricts the applications to vertex focused graphs and cannot be efficiently extended to edge focused graphs like social networks. Applications of current systems are mostly limited to images and document references. Beyond the graph applications, this work also explored the usage of convolutional neural networks for intelligent character recognition in a novel way. Most systems define Intelligent Character Recognition as either a recurrent classification problem or image classification. This achieves great performance in a limited environment but does not generalize well on real world applications. This work defines intelligent Character Recognition as a segmentation problem which we show to provide many benefits. The goal of this work was to explore alternatives to current graph neural networks implementations as well as exploring new applications of such system. This work also focused on improving Intelligent Character Recognition techniques on isolated words using deep learning techniques. Due to the contrast between these to contributions this documents was divided into Part I focusing on the graph work, and Part II focusing on the intelligent character recognition work