Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings

The tasks in fine-grained opinion mining can be regarded as either a token-level sequence labeling problem or as a semantic compositional task. We propose a general class of discriminative models based on recurrent neural networks (RNNs) and word embeddings that can be successfully applied to such tasks without any taskspecific feature engineering effort. Our experimental results on the task of opinion target identification show that RNNs, without using any hand-crafted features, outperform feature-rich CRF-based models. The RNNs based on the long shortterm memory (LSTM) architecture deliver the best results outperforming previous methods including top performing systems in the SemEval’14 evaluation campaign.