A valuable small subset strategically selected from massive online reviews is beneficial to improve consumers’ decision-making efficiency in e-commerce. Existing review selection methods primarily concentrate on the informativeness of reviews and aim to find a subset of reviews that can reflect the informational properties of the original review set. However, changes in consumers’ review diets during the two-phase decision process are not fully considered. In this study, we propose a novel review selection problem of finding a diet-matched review subset with high diversity and representativeness, which can better adapt to consumers’ review-diet conversion from attribute-oriented to experience-oriented reviews between two decision phases. A novel decision-phase-based review selection method named DPRS is further proposed, which involves two steps: review classification and review selection. In the review classification step, the probability of a review being attribute-oriented or experience-oriented is estimated by prior knowledge-aware attentive neural network. In the second step, a novel heuristic algorithm, namely, stepwise non-dominated selection with superiority strategy, is introduced to seek the solution to the review selection problem. Extensive experiments on a real-world dataset demonstrate that DPRS outperforms state-of-the-art methods in terms of both review classification and review selection.