A Survey of Sentiment Analysis Based on Transfer Learning <s> C. SENTIMENT ANALYSIS BASED ON TRANSFER LEARNING <s> A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. <s> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> C. SENTIMENT ANALYSIS BASED ON TRANSFER LEARNING <s> Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper, we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed. <s> BIB002 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> C. SENTIMENT ANALYSIS BASED ON TRANSFER LEARNING <s> Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. <s> BIB003
Transfer learning is the method that uses the similarity of data, data distribution, model, task and so on to apply the knowledge already learned in one domain to the new domain. Through transfer learning, the labeled data can be used to construct the model and be used in the target domain data to increase the annotation of the target data. Before we introduce different sentiment analysis methods based on transfer learning, we first describe some notations and categories that be used in the paper. Domain: Consists of data features and feature distributions, including the source domain and domain, where the source domain exists knowledge and the target domain needs to be learned. Task: Includes the objective function and learning outcomes for the purpose of learning. In a review article made by Pan and Yang BIB001 , a more authoritative definition of transfer learning has been made, and according to the learning method, the transfer learning is divided into four categories, namely instancetransfer approach, feature-representation-transfer approach, parameter-transfer approach and relational-knowledgetransfer approach. Zhang and Yang  also introduced Multi-Task Learning (MTL), analyzed the characteristics of different methods, discussed the combination of MultiTask Learning (MTL) and other learning paradigms. Weiss et al. BIB003 conducted a detailed survey about the related methods and applications of transfer learning, divided the transfer learning methods into two categories: homogeneous and heterogeneous, and discussed the negative transfer problem. At the same time, many scholars have made surveys on transfer learning applications, such as research  on Visual Domain Adaptation and research BIB002 on Activity Recognition. In the next section, the transfer learning methods applied in sentiment analysis will be introduced, and it will be discussed comprehensively in three categories: parametertransfer methods, instance-transfer methods and featurerepresentation-transfer methods.