Sentiment classification for early detection of health alerts in the chemical textile domain

In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.

Towards the Design of a Textile Chemical Ontology

The main goal of this paper is to present the initial version of a Textile Chemical Ontology, to be used by textile professionals with the purpose of conceptualising and representing the banned and harmful chemical substances that are forbidden in this domain. After analysing different methodologies and determining that “Methontology” is the most appropriate for the purposes, this methodology is explored and applied to the domain. In this manner, an initial set of concepts are defined, together with their hierarchy and the relationships between them. This paper shows the benefits of using the ontology through a real use case in the context of Information Retrieval. The potentiality of the proposed ontology in this preliminary evaluation encourages extending the ontology with a higher number of concepts and relationships, and validating it within other Natural Language Processing applications.

CIMBNC- Combination and interactions of three kinds of nanoclays structural modifiers, for the colored bio-nanocomposites optimization

Biopolymers do not have competitive prices, which has prevented their industrial exploitation on a global scale so far. In this context, Using nanoclays, improvements in certain biopolymer properties, mainly mechanical and thermal, have been achieved. However, research has been much less focused on changing optical properties through the incorporation of nanoclays. At the same time, current research has focused on obtaining nanopigments, by organic dyes adsoptions into different nanoclays in order to achieve sustainable colouring and high performance materials. By combining advances in these lines of research, biodegradable composites with optimal mechanical and optical properties can be obtained. The aim of this work is to find the optimal formulation of naturally sourced nanopigments, incorporate them into a biological origin epoxy resin, and obtain a significant improvement in their mechanical, and optical properties. We combine three structural modifiers in the nanopigment synthesis: surfactant, silane and mordant salt. The latter was selected in order to replicate the mordant textile dyeing with natural dyes. Using a Taguchi’s desing L8, we look for the effect of the presence of the modifiers, the pH acidification, and the interactions effect between the synthesis factors. Three natural dyes were selected: chlorophyll, beta-carotene, and beetroot extract. Furthermore we use two kinds of laminar nanoclays, differentiated by the ion exchange charge: montmorillonite, and hydrotalcite. Then the thermal, mechanical and colorimetric characterization of the bionanocomposite materials was carried out. The optimal conditions to obtain the best bionanocomposite materials are using acid pH, and modifying the nanoclays with mordant and surfactant.