Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling

Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). Recently, many methods have made progress on these two tasks. However, few works aim at extracting opinion targets and opinion words as pairs. In this paper, we propose a novel sequence labeling subtask for ABSA named TOWE (Target-oriented Opinion Words Extraction), which aims at extracting the corresponding opinion words for a given opinion target. A target-fused sequence labeling neural network model is designed to perform this task. The opinion target information is well encoded into context by an Inward-Outward LSTM. Then left and right contexts of the opinion target and the global context are combined to find the corresponding opinion words. We build four datasets for TOWE based on several popular ABSA benchmarks from laptop and restaurant reviews. The experimental results show that our proposed model outperforms the other compared methods significantly. We believe that our work may not only be helpful for downstream sentiment analysis task, but can also be used for pair-wise opinion summarization.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Aspect-oriented Opinion Extraction SemEval-2014 Task-4 IOG Restaurant 2014 (F1) 80.02 # 5
Laptop 2014 (F1) 71.35 # 5
Restaurant 2015 (F1) 73.25 # 5
Restaurant 2016 (F1) 81.69 # 5