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. Our framework is flexible, allows us to incorporate other linguistic features, and achieves results that rival the top performing systems in SemEval-2014.

@inproceedings{liu-joty-meng-emnlp-15, abstract = {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. Our framework is flexible, allows us to incorporate other linguistic features, and achieves results that rival the top performing systems in SemEval-2014.}, address = {Lisbon, Portugal}, author = {Liu, Pengfei and Joty, Shafiq and Meng, Helen}, booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing}, pages = {1433--1443}, publisher = {ACL}, series = {EMNLP'15}, title = {Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings}, url = {http://aclweb.org/anthology/D15-1168}, year = {2015} }