  This paper proposes a novel lifelong learning (LL) approach to sentiment
classification. LL mimics the human continuous learning process, i.e.,
retaining the knowledge learned from past tasks and use it to help future
learning. In this paper, we first discuss LL in general and then LL for
sentiment classification in particular. The proposed LL approach adopts a
Bayesian optimization framework based on stochastic gradient descent. Our
experimental results show that the proposed method outperforms baseline methods
significantly, which demonstrates that lifelong learning is a promising
research direction.
