Purpose: An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science). Design/methodology/approach: The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials. Findings: The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches. Originality/value: The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

Business management and, more generally, decision makers, are increasingly aware of the importance of corporate social responsibility and ethical choices within the strategic business vision. The number of tools (e.g., board of directors, organization actions), levers (e.g., cultural, social example of direct boss) and rules (e.g., protocols, certifications, law decrees) available, however, makes it difficult for management to identify the set of best practices to be adopted within its own organization. Further, the task is even more difficult when management is called upon to choose these tools for life-long learning programs intended for company staff as well as for new hires. The Italian Association for Managerial Training has promoted a survey that pays particular attention to the «ethical choices» and «behaviors» to be adopted in the organization’s management and their training programs. The results of the survey have been modelled through «Carroll’s conceptual framework» that, as known, is made of two parts: the most cited CSR pyramid and the least mentioned, but equally important, descriptive types of management. In this work, it has been employed a two steps multivariate analysis, employing an Exploratory Factor Analysis (EFA) and a Structural Equation Modelling (SEM). EFA has been used to identify Carroll’s descriptive types (or profiles), while SEMs were employed to verify the plausibility of the causal models that represent, in turn, thought experiments simulating «ethical dilemmas» useful for the company’s management during its decision making. The models identified, readable in the form of simple «heuristics», are interpreted in the light of Carroll’s «descriptive types» of management (i.e., moral, immoral and amoral). Thereby, any organization, even of a small size, interested in adopting «sustainable policies», can make use of the identified models to establish which guidelines can be adopted by the management during her/his decision making, and, according to Carroll, «to isolate the ethical or moral component of CSR and relate it to perspectives that reflect the three major ethical approaches to management», with the overall objective of managing with «stakeholders in an ethical or moral fashion».

The deployment of semantic web in the context of learning requires a multidimensional consideration of critical issues mostly related with the integration of Semantic Web technologies into the learning context. The main purpose of this article is to discuss in depth the concept of domain ontologies and their role for the effectiveness of semantic web-based e-learning applications. The representation of the learning content and the exploitation of domain ontologies are analysed in the context of a semantic-based prototype, which will be the basis for an FP7 STREP Project proposition. The knowledge acquisition process and the continuous enrichment of the learning content base is recognised as key functions towards high learning outcomes. The main contribution of this article relates to the understanding of ontological challenges for the design and implementation of e-learning systems.