Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Tao Chen, Ruifeng Xu*, Yulan He, Xuan Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, 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 three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach 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 several benchmarking datasets.
Original languageEnglish
Pages (from-to)221-230
Number of pages10
JournalExpert Systems with Applications
Early online date9 Nov 2016
Publication statusPublished - 15 Apr 2017

Bibliographical note

© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (


  • natural language processing
  • sentiment analysis
  • deep neural network


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