  Influence maximization (IM) aims to find seed users on an online social
network to maximize the spread of information about a target product through
word-of-mouth propagation among all users. Prior IM methods mostly focus on
maximizing the overall influence spread, which assumes that all users are
potential customers of the product and that more exposure leads to higher
benefits. However, in real-world scenarios, some users who dislike the product
may express and spread negative opinions, damaging the product's reputation and
lowering its profit. This paper investigates the opinion-aware influence
maximization (OIM) problem, which finds a set of seed users to maximize the
positive opinions toward the product while minimizing the negative opinions. We
propose a novel algorithm for the OIM problem. Specifically, after obtaining
the users with positive and negative opinions towards the product from
historical data, we design a reverse reachable set-based method for
opinion-aware influence estimation and a sandwich approximation algorithm for
seed set selection. Despite the NP-hardness and non-submodularity of OIM, our
algorithm achieves a data-dependent approximation factor for OIM. Experimental
results on three real-world datasets demonstrate that our algorithm improves
the spread of positive opinions while reducing the spread of negative opinions
compared to existing methods.
