This paper proposes an ensemble learning model for opinion mining on food reviews. The proposed model is built on an ensemble of decision trees called Random classification forest. This model performs the task of classifying sentiment about food as positive, negative, or neutral. The ensemble learning model was evaluated on two scenarios, which we built based on important features of the reviews. The experimental results on the food reviews data set have shown the effectiveness of the proposed model.