The goal of cross-domain sentiment classification is to utilise useful information in the source
domain to help classify sentiment polarity in the target domain, which has a large number of …

Appropriately handling unexpected events during the construction, development, and
operation stages of scenic areas and ensuring the personal and property safety of tourists …

Abstract Domain adaptation is proposed to improve the recognition performance of the
domain shift or the dataset bias. The domain shift is a very common problem, which is …

Text sentiment classification is a fundamental sub-area in natural language processing. The
sentiment classification algorithm is highly domain-dependent. For example, the phrase …

Large-scale datasets are driving the rapid developments of deep convolutional neural
networks for visual sentiment analysis. However, the annotation of large-scale datasets is …

The difficulty of cross-domain text sentiment classification is that the data distributions in the
source domain and the target domain are inconsistent. This paper proposes an attention …

Cross Domain Sentiment Analysis (CDSA) is a method that uses rich and quality-labeled
source domain data to identify the sentiments of poorly or without labeled target data. In the …

The lack of sentiment resources in poor resource languages poses challenges for the
sentiment analysis in which machine learning is involved. Cross-lingual and semi …

Single-source domain adaptation (SSDA) for emotion classification aims to leverage useful
information in a source domain to help predict emotional polarity in a target domain in a …

Cross-domain sentiment classification transfers the knowledge from the source domain to
the target domain lacking supervised information for sentiment classification. Existing cross …