Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis

By Haiyun Peng, Lu Xu, Lidong Bing, Fei Huang, Wei Dang Lu, Luo Si. AAAI Conference on Artificial Intelligence, 2019

513 citations recorded by the source corpus.

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/a2a6e1e18d1939622380ae2a2adac0dfdc9799d7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lidong Bing
2. Wenxuan Zhang
3. Wai Lam
4. Xin Li
5. Yang Deng
6. Wei Dang Lu
7. Yifei Yuan
8. Lu Xu

Citations in the corpus

• DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis (2021, background): “Both industry and academia have realized the importance of the relationship between aspect term and sentence [3, 40, 17].”
• Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020, methodology): “When M ≪ n (empirically, we found n can be up to 80 in our datasets, and we set M ∈ [2 , 6] ), this complexity is better than the existing work with complexity O ( n 2 ) (Peng et al., 2019).”
• Aspect Sentiment Quad Prediction as Paraphrase Generation (2021, methodology): “C L ] 2 O c t 2021 prediction performance hinges on the accuracy of every step (Peng et al., 2020; Chen et al., 2020).”