Detection and Analysis of Trend Topics for Global Scientific Literature Using Feature Selection Based on Gini-Index

As the volume and diversity of scientific resources grows, trend detection and analysis have become much more important issues. A variety of trend detection, characterization, evaluation and visualization methodologies have been introduced for various application domains. In this paper, we consider detection of temporal trends for topics using feature selection and extraction of additional information from the subtopics of them, for the Global Trends Briefing (GTB) dataset. Thus, we propose a novel trend detection method using feature selection based on the Improved Gini-Index (I-GI) algorithm, which can obtain representative features for given topics. Second, with those features, we extract subtopics for the topics and visualize temporal/emerging/upward/ downward trends with them. Third, utilizing the relations among the subtopics, we obtain relevant documents and seed sentences that co-occur with the upper features for the topic. In addition, we can extract information to forecast future trends relevant to the issues: for example, financial market or emerging technology. In the experimental results, we could obtain good representative features, more specific trends for the topics, and additional useful information.