Crowdsourcing based scientific issue tracking with topic analysis

Abstract

With the advancement of web technologies, many people are participating in the information production and distribution process in the Web environment. In addition, many researchers have been interested in research on refining useful information using topic based recommendation system because the amount and complexity of web information is rapidly increasing. The proposed approach performs typical scientific data collection and then analyzes seed problem keywords using multi-level documents based on crowd sourcing. We then used the LDA algorithm to create a cluster of scientific themes to generate issue keywords that are responsive to the scientific trend issues. As a result, our approach suggests a methodology for recommending clusters of related issues when scientific issues are raised in each context.