Neural Networks for Open Domain Targeted Sentiment

Open domain targeted sentiment is the joint information extraction task that finds target mentions together with the senti-ment towards each mention from a text corpus. The task is typically modeled as a sequence labeling problem, and solved us-ing state-of-the-art labelers such as CRF. We empirically study the effect of word embeddings and automatic feature combi-nations on the task by extending a CRF baseline using neural networks, which have demonstrated large potentials for sentiment analysis. Results show that the neural model can give better results by significantly increasing the recall. In ad-dition, we propose a novel integration of neural and discrete features, which com-bines their relative advantages, leading to significantly higher results compared to both baselines.