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

Abstract

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.

GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

“…DREGCN is short for the official DREGCN+CNN+BERT due to its better performance. WHW (Peng et al, 2020) develops a two-stage framework to address aspect term extraction, aspect sentiment classification, and opinion extraction. TAS-BERT (Wan et al, 2020) proposes a method based on BERT-Base that can capture the dependence on both aspect terms and categories for sentiment prediction.…”

“…Hu et al (2019) consider the ATE as a span extraction question, and extract aspect term and its sentiment polarity using a pipeline approach. There are some other approaches to address these two tasks (Li et al, 2019c;He et al, 2019;Liang et al, 2020a;Peng et al, 2020;Wan et al, 2020;Liang et al, 2020b;Chen and Qian, 2020). However, almost all of previous models do not concern the imbalance of labels in such sequence labeling tasks.…”

Deep2s: Improving Aspect Extraction in Opinion Mining With Deep Semantic Representation

“…In (1), the most frequent opinion targets are considered with respect to their frequency and it means that identifying (or losing) the most frequent opinion targets is identifying (or losing) the most important opinion targets. And in (2), all the opinion targets have the same importance.…”

“…Then, we propose Deep2S, a hybrid rule-based approach to improve the performance of aspect extraction by combining syntactic and semantic rules. (2) Deep semantic representation such as Abstract Meaning Representation, is explored to capture deep and rich semantic information in customer reviews. This paper hypothesizes that the semantic relations between aspects and opinion words or other entities can be captured by AMR subgraphs.…”

Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning

“…Note that(Peng et al, 2020) also proposes a related model for TOWE based on multitask deep learning. However, the models in this work actually predict general opinion words that are not necessary tied to any target word.…”