Abstract
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://140.115.51.136:5000.
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Chung, J.KC., Wu, CE., Tsai, R.TH. (2014). Polarity Detection of Online Reviews Using Sentiment Concepts: NCU IISR Team at ESWC-14 Challenge on Concept-Level Sentiment Analysis. In: Presutti, V., et al. Semantic Web Evaluation Challenge. SemWebEval 2014. Communications in Computer and Information Science, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-12024-9_7
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DOI: https://doi.org/10.1007/978-3-319-12024-9_7
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