Active learning experiments for the classification of smoking tweets

Aki Härmä, Andrey Polyakov, Ekaterina Chernyak

Research output: Contribution to journalConference article in journalAcademicpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)193-203
Number of pages11
JournalCEUR Workshop Proceedings
Volume2142
Publication statusPublished - 2018
Externally publishedYes
Event1st International Workshop on Artificial Intelligence in Health - Stockholm, Sweden
Duration: 13 Jul 201814 Jul 2018
Conference number: 1

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