Sentiment analysis is recognized as one of the most important sub-areas in Natural Language Processing (NLP) research, where understanding implicit or explicit sentiments expressed in social media contents is valuable to customers, business owners, and other stakeholders. Researchers have recognized that the generic sentiments extracted from the textual contents are inadequate, thus, Aspect Based Sentiment Analysis (ABSA) was coined to capture aspect sentiments expressed toward specific review aspects. Existing ABSA methods not only treat the analytical problem as single-label classification that requires a fairly large amount of labelled data for model training purposes, but also underestimate the entity aspects that are independent of certain sentiments. In this study, we propose a transfer learning based approach tackling the aforementioned shortcomings of existing ABSA methods. Firstly, the proposed approach extends the ABSA methods with multi-label classification capabilities. Secondly, we propose an advanced sentiment analysis method, namely Aspect Enhanced Sentiment Analysis (AESA) to classify text into sentiment classes with consideration of the entity aspects. Thirdly, we extend two state-of-the-art transfer learning models as the analytical vehicles of multi-label ABSA and AESA tasks. We design an experiment that includes data from different domains to extensively evaluate the proposed approach. The empirical results undoubtedly exhibit that the proposed approach outperform all the baseline approaches.

中文翻译：

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走向多标签情感分析：一种基于转移学习的方法

情感分析被认为是自然语言处理（NLP）研究中最重要的子领域之一，在该领域中，理解社交媒体内容中表达的内隐或外显情感对客户，企业主和其他利益相关者都很有价值。研究人员已经认识到从文本内容中提取的一般情感是不充分的，因此，基于方面的情感分析（ABSA）被创造出来，以捕获针对特定评论方面表达的方面情感。现有的ABSA方法不仅将分析问题视为单标签分类，需要大量标签数据用于模型训练目的，而且低估了实体方面独立于某些情绪。在这项研究中，我们提出了一种基于迁移学习的方法，以解决现有ABSA方法的上述缺点。首先，该方法扩展了ABSA方法的多标签分类能力。其次，我们提出了一种先进的情感分析方法，即“方面增强情感分析”（AESA），以考虑实体方面将文本分为情感类别。第三，我们扩展了两个最先进的转移学习模型，作为多标签ABSA和AESA任务的分析工具。我们设计了一个包含来自不同领域的数据的实验，以广泛地评估所提出的方法。实证结果无疑表明，所提出的方法优于所有基线方法。