In this paper, we present our system that participated in the Polarity Detection task, the elementary task in the ESWC-14 Challenge on Concept-Level Sentiment Analysis. In addition to traditional Bag-of-Words features, we also employ state-of-the-art Sentic API to extract concepts from documents to generate Bag-of-Sentiment-Concepts features. Our previous work SentiConceptNet serves as the reference concept-based sentiment knowledge base for concept-level sentiment analysis. Experimental results on our development set show that adding Bag-of-Sentiment-Concepts can improve the accuracy by 1.3 %, indicating the benefit of concept-level sentiment analysis. Our demo website is located at http://184.108.40.206:5000.