A Methodology to Quickly Perform Opinion Mining and Build Supervised Datasets Using Social Networks Mechanics

Abstract

Social Networking Sites (SNS) offer a full set of possibilities to perform opinion studies such as polling or market analysis. Normally, artificial intelligence techniques are applied, and they often require supervised datasets. The process of building these is complex, time-consuming and expensive. In this paper, we propose to assist the labelling task by taking advantage of social network mechanics. In order to do that, we introduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">co-retweet</i> relation to build a graph that allows us to propagate user labels to their similarity neighbourhood. Therefore, it is possible to iteratively build supervised datasets with significant less human effort and with higher accuracy than other weak-supervision techniques. We tested our proposal with 3 datasets labelled by an expert committee, and results shows that it outperforms other weak-supervision techniques. This methodology may be adapted to other social networks and topics, and it is relevant for applications like informed decision-making (e.g. content moderation), specially when interpretability is required.