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Table 5: Accuracies of models on four CDLT datasets from M:Mov_en and R:Res_en datasets.
['[EMPTY]', 'Long to Short', 'Long to Short', 'Short to Long', 'Short to Long']
Which between domain and text length should we consider to achieve a better performance? To answer this question, we combine Cross Domain Transfer (CDT) and Cross Length Transfer (CLT) into one task: Cross Domain and Length Transfer (CDLT) and compare the performance of CDT and CLT models on the task. We use the Mov_en and Res_en datasets to create four CDLT datasets, and check which between the CDT model NeuSCL+CNN and the CLT model LeTraNets achieves a higher increase in performance. We find that NeuSCL+CNN performs worse, obtaining accuracies worse than that of the no-transfer CNN baseline. LeTraNets performs better, obtaining significant increase in performance from the baseline. This shows that solving the non-transferability of length is more important to achieve a more effective sentiment classifier.