Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set
Table 2: Single-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SFA Pan et al. (2010), KMM Huang et al. (2007), CORAL Sun et al. (2016) and TR-TrAdaBoost Huang et al. (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01.
['Method', 'D→B', 'E→B', 'K→B', 'B→D', 'E→D', 'K→D', 'B→E', 'D→E', 'K→E', 'B→K', 'D→K', 'E→K']
Results in single-source setting. We considered all possible combinations of source and target domains in this experiment, and we improve the results in each and every case. Without exception, the accuracy rates reached by the transductive string kernels are significantly better than the best baseline string kernel Giménez-Pérez et al. , according to the McNemar’s test performed at a confidence level of 0.01. The highest improvements (above 2.7%) are obtained when the source domain contains Books reviews and the target domain contains Kitchen reviews. As in the multi-source setting, we obtain much better results when the transductive classifier is employed for the learning task. In all cases, the accuracy rates of the transductive classifier are more than 2% better than the best baseline string kernel. Remarkably, in four cases (E→B, E→D, B→K and D→K) our improvements are greater than 4%. The improvements brought by our transductive classifier based on string kernels are statistically significant in each and every case. In comparison with SFA Pan et al. With respect to KMM Huang et al. Remarkably, we surpass the other state-of-the-art approaches Sun et al.