DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
Table 1: Datasets from SemEval and Twitter.
['Datasets SL', 'Datasets #PO', 'Train 941', 'Dev 32', 'Test 340', 'Total 1,313']
We conduct experiments on two datasets from the SemEval challenges and one English Twitter dataset. SL comes from SemEval 2014 Pontiki et al. SemEval 2016 Pontiki et al. We keep the official data division of these datasets for the training set, validation set, and testing set. The reported results of SL and SR are averaged scores of 10 runs. ST consists of English tweets. Due to lack of standard train-test split, we report the ten-fold cross-validation results of ST as done in Mitchell et al. ; Zhang et al. The evaluation metric is F1 score based on the exact match of aspect term and its polarity.