Learning Sentimental Influences from Users' Behaviors

TL;DR

This paper introduces a novel model for learning sentimental influences from user behaviors by using latent influence and susceptibility matrices, improving over previous pairwise approaches and revealing new insights into user influence dynamics.

Contribution

The paper proposes a low-dimensional, coupled influence model that incorporates sentiment polarities, along with an efficient optimization algorithm, advancing the understanding of sentimental influence in social networks.

Findings

Model outperforms state-of-the-art methods on Microblog data

Learned influence distributions reveal interesting user influence patterns

Efficient optimization reduces computational costs

Abstract

Modeling interpersonal influence on different sentimental polarities is a fundamental problem in opinion formation and viral marketing. There has not been seen an effective solution for learning sentimental influences from users' behaviors yet. Previous related works on information propagation directly define interpersonal influence between each pair of users as a parameter, which is independent from each others, even if the influences come from or affect the same user. And influences are learned from user's propagation behaviors, namely temporal cascades, while sentiments are not associated with them. Thus we propose to model the interpersonal influence by latent influence and susceptibility matrices defined on individual users and sentiment polarities. Such low-dimensional and distributed representations naturally make the interpersonal influences related to the same user coupled with each other, and in turn, reduce the model complexity. Sentiments act on different rows of parameter matrices, depicting their effects in modeling cascades. With the iterative optimization algorithm of projected stochastic gradient descent over shuffled mini-batches and Adadelta update rule, negative cases are repeatedly sampled with the distribution of infection frequencies users, for reducing computation cost and optimization imbalance. Experiments are conducted on Microblog dataset. The results show that our model achieves better performance than the state-of-the-art and pair-wise models. Besides, analyzing the distribution of learned users' sentimental influences and susceptibilities results some interesting discoveries.