A Novel Idea Generation Method for the Internet of Digital Reality Era: The Spinning Aufheben Method

Internet of Digital Reality (IoD) will be one of the essential next-generation information technologies. The content and presentation of information are the most important aspects that will make IoD work efficiently. However, the generation of ideas for IoD has not much progress in discussion because formalizing it is difficult. This paper presents an outline of the Spinning Aufheben (SA) method, which is a novel idea generation method, its application and model, validity, actual cases of the first application of the author, and potential social impact. Aufheben is one of the common mechanisms for generating ideas from two elements. This method enables the infinite generation of ideas by rotating three elements of a dialectic. We also present the result of pilot projects on 51 university students to determine its effectivity as an application for helping them determine future career plans after graduation. As a result, 46 students identified their career goals. The students expressed appreciation of the career search results using the SA methood

Social Media Text Classification by Enhancing Well-Formed Text Trained Model

Social media are a powerful communication tool in our era of digital information. The large amount of user-generated data is a useful novel source of data, even though it is not easy to extract the treasures from this vast and noisy trove. Since classification is an important part of text mining, many techniques have been proposed to classify this kind of information. We developed an effective technique of social media text classification by semi-supervised learning utilizing an online news source consisting of well-formed text. The computer first automatically extracts news categories, well-categorized by publishers, as classes for topic classification. A bag of words taken from news articles provides the initial keywords related to their category in the form of word vectors. The principal task is to retrieve a set of new productive keywords. Term Frequency-Inverse Document Frequency weighting (TF-IDF) and Word Article Matrix (WAM) are used as main methods. A modification of WAM is recomputed until it becomes the most effective model for social media text classification. The key success factor was enhancing our model with effective keywords from social media. A promising result of 99.50% accuracy was achieved, with more than 98.5% of Precision, Recall, and F-measure after updating the model three times