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

• Published in: Expert systems with applications Vol. 72; pp. 221 - 230
• Main Authors: Chen, Tao, Xu, Ruifeng, He, Yulan, Wang, Xuan
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.04.2017; Elsevier BV
• Subjects: Artificial neural networks; Classification; Data mining; Datasets; Deep neural network; Natural language processing; Neural networks; Performance enhancement; Sentences; Sentiment analysis; Studies; Sentiment analysis; Deep neural network; Natural language processing
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2016.10.065

•A divide-and-conquer method classifying sentence types before sentiment analysis.•Classifying sentence types by the number of opinion targets a sentence contain.•A data-driven approach automatically extract features from input sentences. 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.