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This article introduces a novel approach for sentiment analysis – the clustering-based sentiment analysis approach. By applying a TF-IDF weighting method, a voting mechanism and importing term scores, an acceptable and stable clustering result can be obtained. The methodology has competitive advantages over the two existing types of approaches: symbolic techniques and supervised learning methods. It is a well-performed, efficient and non-human participating approach to solving sentiment analysis problems.

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