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.