Active learning experiments for the classification of smoking tweets.
Aki Härmä, Andrey Polyakov, Ekaterina Chernyak · Research Publications (Maastricht University) · 2018
In automated health services based on text and voice interfaces, there is need to be able to understand what the user is talking about, and what is the attitude of the user towards a subject. Typical machine learning methods for text analysis require a lot of annotated data for the training. This is often a problem in addressing specific and possibly very personal health care needs. In this paper we propose an active learning algorithm for the training of a text classifier for a conversational therapy application in the area of health behavior change. A new active learning algorithm, Query by Embedded Committee (QBEC), is proposed in the paper. The methods is particularly suitable for the text classification task in a dynamic environment and gives a good performance with realistic test data.