Neural Networks for Open Domain Targeted Sentiment
Meishan Zhang, Yue Zhang, Duy Tin Vo · 2015
Open domain targeted sentiment is the joint information extraction task that finds target mentions together with the sentiment towards each mention from a text corpus.The task is typically modeled as a sequence labeling problem, and solved using state-of-the-art labelers such as CRF.We empirically study the effect of word embeddings and automatic feature combinations 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 addition, we propose a novel integration of neural and discrete features, which combines their relative advantages, leading to significantly higher results compared to both baselines.