Aspect-based sentiment summarization systems generally use sentences associated with relevant aspects extracted from the reviews as the basis for summarization. However, in real reviews, a single sentence often exhibits several aspects for opinions. This paper proposes a two-stage segmentation model to address the challenge of identifying multiple single-aspect and single-polarity units in one sentence, namely aspect-based sentence segmentation. Our model deals with both issues of aspect change and polarity change occurring in the input sentence. Experiments on restaurant reviews show that our model outperforms state-of-the-art linear text segmentation methods.