A Statistical Parsing Framework for Sentiment Classification
Table 3: Statistical information of datasets. #Negative and #Positive are the number of negative instances and positive instances, respectively. lavg is average length of sentences in the dataset, and |V| is the vocabulary size.
['Dataset', 'Size', '#Negative', '#Positive', '[ITALIC] lavg', '| [ITALIC] V|']
On the dataset MPQA, the accuracy of s.parser increases by 0.5%, 1.1%, and 14.8% from the best results of SVM, MNB, and LM, respectively. Compared to Voting-w/Rev and HardRule, s.parser achieves 4.5% and 4.4% improvements over them. The RAE-pretrain achieves better results than other methods on this dataset, because the word embeddings pre-trained on Wikipedia can leverage smoothing to relieve the sparsity problem in MPQA. If we do not use any external resources (i.e. Wikipedia), the accuracy of RAE on MPQA is 85.7% which is lower than Tree-CRF and s.parser. The results indicate that s.parser achieves the best result if no external resource is employed.