An efficient approach for opinion mining from skewed twitter corpus using under sampling approach

Data Mining is an efficient technique for knowledge discovery from existing databases. The existing algorithms performance degrades when applied to the imbalance dataset. The imbalance nature of twitter data set also hinders the process of efficient knowledge discovery. In this paper, we proposed an efficient approach for knowledge discovery from imbalance datasets specifically designed for opinion mining. The proposed Under Sampled Imbalance Data Learning (USIDL) approach uses the unique technique for under sampling the instances from majority subset. The experimental results suggest that the proposed approach performs better than the existing C4.5 algorithm on seven evaluation metrics.