We consider the combinatorial semi-bandit problem and present a new algorithm with a best-of-both-worlds regret guarantee; the regrets are bounded near-optimally in the stochastic and adversarial regimes. In the stochastic regime, we prove a variance-dependent regret bound depending on the tight suboptimality gap introduced by Kveton et al. (2015) with a good leading constant. In the adversarial regime, we show that the same algorithm simultaneously obtains various data-dependent regret bounds. Our algorithm is based on the follow-the-regularized-leader framework with a refined regularizer and adaptive learning rate. Finally, we numerically test the proposed algorithm and confirm its superior or competitive performance over existing algorithms such as Thompson sampling in most settings.