Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained
opinion polarity towards a specific aspect, is a challenging subtask of
sentiment analysis (SA). In this paper, we construct an auxiliary sentence from
the aspect and convert ABSA to a sentence-pair classification task, such as
question answering (QA) and natural language inference (NLI). We fine-tune the
pre-trained model from BERT and achieve new state-of-the-art results on
SentiHood and SemEval-2014 Task 4 datasets.

本研究使用 BERT 模型，将 ABSA 转换为句对分类任务，取得了 SentiHood 和 SemEval-2014 Task 4 数据集的最新最佳结果，旨在识别特定方面的细粒度观点极性。

基于 BERT 与构建辅助语句的方面情感分析

Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing  Auxiliary Sentence

In this paper, we introduce the task of targeted aspect-based sentiment
analysis. The goal is to extract fine-grained information with respect to
entities mentioned in user comments. This work extends both aspect-based
sentiment analysis that assumes a single entity per document and targeted
sentiment analysis that assumes a single sentiment towards a target entity. In
particular, we identify the sentiment towards each aspect of one or more
entities. As a testbed for this task, we introduce the SentiHood dataset,
extracted from a question answering (QA) platform where urban neighbourhoods
are discussed by users. In this context units of text often mention several
aspects of one or more neighbourhoods. This is the first time that a generic
social media platform in this case a QA platform, is used for fine-grained
opinion mining. Text coming from QA platforms is far less constrained compared
to text from review specific platforms which current datasets are based on. We
develop several strong baselines, relying on logistic regression and
state-of-the-art recurrent neural networks.

本文提出了有针对性的方面情感分析任务，针对用户评论中提到的实体提取细粒度信息，通过对一个或多个实体的每个方面的情感进行识别来扩展基于方面的情感分析和有针对性的情感分析。在此任务的基础上，我们引入了 SentiHood 数据集，并开发了几种基于逻辑回归和最先进的循环神经网络的强大基准。