Journal
NEUROCOMPUTING
Volume 459, Issue -, Pages 1-9Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2021.06.045
Keywords
Sentiment analysis; ABSA; Joint model; Deep learning
Categories
Funding
- National Natural Science Foundation of China [62076048]
- Science and Technology Innovation Foundation of Dalian [2020JJ26GX035]
Ask authors/readers for more resources
This paper focuses on aspect-term extraction and aspect sentiment classification in aspect-based sentiment analysis, and proposes a novel end-to-end generative model to address these tasks, which demonstrates competitive performance in experiments.
This paper focuses on two related sub-tasks of aspect-based sentiment analysis, namely aspect-term extraction and aspect sentiment classification. The former aims to extract aspect-terms from given sentences and the latter aims to identify the sentiment polarity expressed on the extracted terms. Considering the practical application, researchers use more joint methods rather than pipeline methods. However, existing joint methods cannot model the interaction between aspect-terms and the sentence they belong to, or consider the relevance among the sentiments of different aspect-terms. In this paper, a novel end-to-end generative model based on encoder-decoder, namely Joint Term-Sentiment Generator (JTSG), is presented to generate all aspect term-polarity pairs. Specifically, a pre-trained model based encoder is used to encode the sentences, and specially, the decoder generates the start and end position to determine an aspect-term, rather than generate aspect-terms themselves. This new generative method contributes to avoid generating incomplete aspect-terms. Experimental results demonstrate that the proposed approach yields competitive performance on three benchmark datasets. (c) 2021 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available