Tracking User Sentiment Changes on Social Networks

We present a time-dependent approach for learning temporal co-variates explaining the subsequent user sentiments in social networks. In most of the existing approaches, we note that the underlying text classification setting, generally used to model user sentiments, is designed to ingest a user comment generated at time t to predict his corresponding sentiment at the same time. Under such constraint, user sentiments can only be given whenever she or he has generated a comment. Furthermore, the evolving historical sentiments are omitted and no anticipation of subsequent sentiments could be made. To alleviate this limitation, we propose a time-dependent approach that takes advantage of historical user comments to learn temporal co-variates that explain their evolving sentiments. We demonstrate that our approach could be used to predict user sentiments at subsequent times ahead. Experimental results on Tweets data, during the Covid-19 pandemic, illustrate the suitability of our approach.