TY - JOUR
AU - 
AB - Article history: Different types of sentences express sentiment in very different ways. Traditional sentence-level senti- Received 9 July 2016 ment classification research focuses on one-technique-fits-all solution or only centers on one special type Revised 1 October 2016 of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences Accepted 21 October 2016 into different types, then performs sentiment analysis separately on sentences from each type. Specif- Available online 9 November 2016 ically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into Keywords: Natural language processing three types according to the number of targets appeared in a sentence. Each group of sentences is then Sentiment analysis fed into a one-dimensional convolutional neural network separately for sentiment classification. Our ap- Deep neural network proach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on sev- eral benchmarking datasets. © 2016 The Authors. Published by Elsevier 
TI - Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN
JF - Expert Systems with Applications
DO - 10.1016/j.eswa.2016.10.065
DA - 2017-04-01
UR - https://www.deepdyve.com/lp/unpaywall/improving-sentiment-analysis-via-sentence-type-classification-using-LSairdoUdt
DP - DeepDyve
ER -