An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach.