A Statistical Parsing Framework for Sentiment Classification
Table 4: Sentiment classification results on different datasets. The top three methods are in bold and the best is also underlined. SVM-m: Support Vector Machine. MNB-m: Multinomial Naïve Bayes. LM-m: Language Model. Voting-w/Rev: Voting with negation rules. HardRule: Rule based method on dependency tree. Tree-CRF: Dependency tree-based method employing conditional random fields. RAE-pretrain: Recursive autoencoders with pre-trained word vectors. MV-RNN: Matrix-vector recursive neural network. s.parser-LongMatch: The longest matching rules are used. s.parser-w/oComb: Without using the combination rules. s.parser: Our method. Some of results are missing (indicated by “-”) in the table as there is no publicly available implementation or they are hard to scale up.
['Method', 'RT-C', 'PL05-C', 'SST', 'RT-U', 'IMDB-U', 'MPQA']
The top three methods on each dataset are in bold, and the best methods are also underlined. The experimental results show that s.parser achieves better performances than other methods on most datasets.