A comparative study of uncertainty based active learning strategies for general purpose twitter sentiment analysis with deep neural networks

Abstract

Active learning is a common approach when it comes to classification problems where a lot of unlabeled samples are available but the cost of manually annotating samples is high. This paper describes a study of the feasibility of uncertainty based active learning for general purpose Twitter sentiment analysis with deep neural networks. Results indicate that the approach based on active learning is able to achieve similar results to very large corpora of randomly selected samples. The method outperforms randomly selected training data when the amount of training data used for both approaches is of equal size.