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

Abstract

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.

Publication DOI: https://doi.org/10.1016/j.eswa.2016.10.065
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: natural language processing,sentiment analysis,deep neural network,Engineering(all),Computer Science Applications,Artificial Intelligence
Publication ISSN: 1873-6793
Last Modified: 20 May 2024 07:17
Date Deposited: 14 Nov 2016 12:00
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2017-04-15
Published Online Date: 2016-11-09
Accepted Date: 2016-10-21
Submitted Date: 2016-07-09
Authors: Chen, Tao
Xu, Ruifeng
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Wang, Xuan

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