Attention-based neural models were employed to detect the different aspects
and sentiment polarities of the same target in targeted aspect-based sentiment
analysis (TABSA). However, existing methods do not specifically pre-train
reasonable embeddings for targets and aspects in TABSA. This may result in
targets or aspects having the same vector representations in different contexts
and losing the context-dependent information. To address this problem, we
propose a novel method to refine the embeddings of targets and aspects. Such
pivotal embedding refinement utilizes a sparse coefficient vector to adjust the
embeddings of target and aspect from the context. Hence the embeddings of
targets and aspects can be refined from the highly correlative words instead of
using context-independent or randomly initialized vectors. Experiment results
on two benchmark datasets show that our approach yields the state-of-the-art
performance in TABSA task.

提出了一种新颖的方法来对目标区分，以便在 TABSA 中检测不同方面和情感极性，利用罕见分量向量调整目标和方面的嵌入来优化嵌入，从而优化了上下文相关信息。

面向目标方面的情感分析感知嵌入

Context-aware Embedding for Targeted Aspect-based Sentiment Analysis

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。