Effective LSTMs for Target-Dependent Sentiment Classification
Table 1: Comparison of different methods on target-dependent sentiment classification. Evaluation metrics are accuracy and macro-F1. Best scores are in bold.
['Method', 'Accuracy', 'Macro-F1']
Comparing between SVM-indep and SVM-dep, we can find that incorporating target information can improve the classification accuracy of a basic SVM classifier. AdaRNN performs better than feature based SVM by making use of dependency parsing information and tree-structured semantic composition. We can find that target-dep is a strong performer even without using lexicon features. It benefits from rich automatic features generated from word embeddings.