Classification and optimization of product review information using soft computing models

A soft computing framework to classify and optimize text-based information extracted from customers' product reviews is proposed in this paper. The soft computing framework performs classification and optimization in two stages. Given a set of keywords extracted from unstructured text-based product reviews, a Support Vector Machine (SVM) is used to classify the reviews into two categories (positive and negative reviews) in the first stage. An ensemble of evolutionary algorithms is deployed to perform optimization in the second stage. Specifically, the Modified micro Genetic Algorithm (MmGA) optimizer is applied to maximize classification accuracy and minimize the number of keywords used in classification. Two Amazon product reviews databases are employed to evaluate the effectiveness of the SVM classifier and the ensemble of MmGA optimizers in classification and optimization of product related keywords. The results are analyzed and compared with those published in the literature. The outputs potentially serve as a list of impression words that contains useful information from the customers' viewpoints. These impression words can be further leveraged for product design and improvement activities in accordance with the Kansei engineering methodology.

Becoming numerate with information and communications technologies in the twenty-first century

This article draws on data from a three-year Australian Research Council-funded study that examined the ways in which young children become numerate in the twenty-first century. We were interested in the authentic problem-solving contexts that we believe are required to create meaningful learning. This being so, our basic tenet was that such experiences should involve the use of information and communications technologies (ICT) where relevant, but not in tokenistic ways. This article highlights learning conditions in which young children can become numerate in contemporary times. We consider ‘academic’ or ‘school-based’ mathematical tasks in the context of a Mathematical Tasks Continuum. This continuum was conceptualised to enable focused and detailed thinking about the scope and range of mathematical tasks that young children are able to engage within contemporary school contexts. The data from this study show that most of the tasks the children experienced in early years mathematics classes were unidimensional in their make up. That is, they focus on the acquisition of specific skills and then they are practiced in disembedded contexts. We suggest that the framework created in the form of the Mathematical Tasks Continuum can facilitate teachers thinking about the possible ways in which they could extend children’s academic work in primary school mathematics, so that the process of becoming numerate becomes more easily related to authentic activities that they are likely to experience in everyday life.