Wordoids: Boid Based Personalized Word Clustering System in Dark Side Ternary Stars

Abstract

Personalized systems are required in many domains. However, gathering training data for personalization from individuals, as is necessary with deep learning, is a difficult and timeconsuming task. With our proposed method, less or no training data is required to adapt to individuals’ preferences, even when they shift over time. We introduce a potential field based method “Dark Side Ternary Stars” which has three components, GAGPL, Wordoids, and EGO. In this paper, we focus on two of them, ”Wordoids”, which adopt extends Boids algorithms to perform individualized classification of keywords by topic and improved our previous work ”GAGPL”, which calculates the individualized semantic orientation of sentences by using learned words per topic. As experimental results, we applied this method to news articles about Japanese professional baseball and we show that our method can obtain individualized semantic orientations and summaries of the article per individual.