Text mining and dimension reduction method application into exploring isomorphic pressures in corporate communication on textual tweet data about sustainability in the energy sector

The study analyses the isomorphism pressures within the context of sustainability by exploring the Twitter communication in the energy sector. Recently, there can be observed the increasing focus on interactive and communicative construction of an institution to understand how the organizations sustain the institutional pressures. The rhetorical commitments that create narrative dynamics in organizational communication are central to institutional diffusion and change. Social Media, Twitter, in particular, has been demonstrated as the new opportunity to explore the linguistic dimension in corporate communications. We propose the use of Social Media linguistic data (tweets with their hashtags and keywords) and the triangulated method (text mining, web mining, and linguistic and content analysis) to examine the tweets´ trends in each company. Based on the institutional theory of organizational communication, the paper examines the relation between the idea of sustainability and isomorphism that leads to the adoption of similar models and attitudes among the organizations. It applies the text mining and correspondence methods within the R software. The energy sector tweets in English (from 2016) were treated by the text mining processes of the statistical linguistic analysis in the R tool. Text mining, involving the linguistic, statistical, and the machine learning techniques reveals and visualizes the latent structures of the content in an unstructured or weakly structured text data in a given collection of documents.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Formally analysing the concepts of domestic violence.

The types of police inquiries performed these days are incredibly diverse. Often data processing architectures are not suited to cope with this diversity since most of the case data is still stored as unstructured text. In this paper Formal Concept Analysis (FCA) is showcased for its exploratory data analysis capabilities in discovering domestic violence intelligence from a dataset of unstructured police reports filed with the regional police Amsterdam-Amstelland in the Netherlands. From this data analysis it is shown that FCA can be a powerful instrument to operationally improve policing practice. For one, it is shown that the definition of domestic violence employed by the police is not always as clear as it should be, making it hard to use it effectively for classification purposes. In addition, this paper presents newly discovered knowledge for automatically classifying certain cases as either domestic or non-domestic violence is. Moreover, it provides practical advice for detecting incorrect classifications performed by police officers. A final aspect to be discussed is the problems encountered because of the sometimes unstructured way of working of police officers. The added value of this paper resides in both using FCA for exploratory data analysis, as well as with the application of FCA for the detection of domestic violence.Formal concept analysis (FCA); Domestic violence; Knowledge discovery in databases; Text mining; Exploratory data analysis; Knowledge enrichment; Concept discovery;

Assembly of an interactive correlation network for the Arabidopsis genome using a novel heuristic clustering algorithm

Peer reviewedPublisher PD

Explaining Explanations in AI

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly

How emergent self organizing maps can help counter domestic violence.

Topographic maps are an appealing exploratory instrument for discovering new knowledge from databases. During the past years, new types of Self Organizing Maps (SOM) were introduced in the literature, including the recent Emergent SOM. The ESOM is used to study a large set of police reports describing a whole range of violent incidents that occurred during the year 2007 in the police region Amsterdam-Amstelland (the Netherlands). It is demonstrated that it provides an exploratory search instrument for examining unstructured text in police reports. First, it is shown how the ESOM was used to discover a whole range of new features that better distinguish domestic from non-domestic violence cases. Then, it is demonstrated how this resulted in a significant improvement in classification accuracy. Finally, the ESOM is showcased as a powerful instrument for the domain expert interested in an indepth investigation of the nature and scope of domestic violence.