Structural Correspondence Learning for Cross-lingual Sentiment Classification with One-to-many Mappings
Table 3: Performance comparisons on the NLP&CC 2013 CLSC data set.
['Method', 'Book', 'DVD', 'Music', 'Average']
From the results, we see that the best performance of co-training is better than that of CL-SCL, but it requires parallel dataset both in training and testing processes. Gui et al. combined co-training and transfer learning strategies. Zhou et al. It integrated the bilingual embedding learning into a unified process, and achieved 80.7% accuracy. Our approach reaches up to 81.4% average accuracy with the fixed parameters. For Books and Music categories, SCL-OM achieves the best accuracy when k=120, m=300, while for DVD, it is when k=110, m=200. The experimental results show that our approach is competitive with the state-of-the-art in cross-language sentiment classification.