Abstract
The extraction of opinion target–word pairs from user reviews has received much attention recently, since it can provide essential information for fine-grained opinion analysis. However, according to our statistics on a large-scale dataset of Chinese reviews, about 60% reviews do not explicitly show opinion targets or words. To investigate this problem, this paper introduces a new task under fine-grained opinion analysis, named A spect- B ased P air-wise O pinion G eneration (ABPOG), which aims to generate opinion target–word pairs based on reviews and aspects. To perform this task, we develop a sequence-to-sequence model for opinion target–word pair generation by extending the pointer–generator network with two approaches: (1) an aspect-aware encoder that receives an additional aspect embedding as input to extract aspect-specific features, (2) two hierarchical decoders including a token-level GRU and a global GRU to generate opinion targets and words jointly. To empirically evaluate our task and model, we develop a multi-aspect dataset for ABPOG based on Chinese automotive reviews. Extensive experiments on our dataset show that our model outperforms several strong baselines adapted from the state-of-the-art aspect-based summarization method. • We introduce a new task of pair–wise opinion generation based on reviews and aspects. • We construct a large–scale multiaspect dataset, which can be used as a benchmark. • We propose a novel model to generate opinion target–word jointly for a given aspect. • The proposed method outperforms several strong baselines and keeps interpretability. • We conduct detailed analyses for better understanding the data and model.
Published Version
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