A Survey of Sentiment Analysis Based on Transfer Learning <s> A. CROSS-DOMAIN SENTIMENT ANALYSIS <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> A. CROSS-DOMAIN SENTIMENT ANALYSIS <s> Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the issue, several knowledge-based models have been proposed to incorporate prior knowledge provided by the user to guide modeling. In this paper, we take a major step forward and show that in the big data era, without any user input, it is possible to learn prior knowledge automatically from a large amount of review data available on the Web. Such knowledge can then be used by a topic model to discover more coherent aspects. There are two key challenges: (1) learning quality knowledge from reviews of diverse domains, and (2) making the model fault-tolerant to handle possibly wrong knowledge. A novel approach is proposed to solve these problems. Experimental results using reviews from 36 domains show that the proposed approach achieves significant improvements over state-of-the-art baselines. <s> BIB002 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> A. CROSS-DOMAIN SENTIMENT ANALYSIS <s> Abstract Opinion target extraction or aspect extraction is the most important subtask of the aspect-based sentiment analysis. This task focuses on the identification of the targets of user's opinions or sentiments from online reviews. In the recent years, syntactic patterns-based approaches have performed quite well and produced significant improvement in the aspect extraction task. However, these approaches are heavily dependent on the dependency parsers which produced syntactic relations following the grammatical rules and language constraints. In contemporary, users do not give much importance to these rules and constraints while expressing their opinions about particular product and neither reviewer websites restrict users to do so. This makes syntactic patterns-based approaches vulnerable. Therefore, in this paper, we are proposing a two-fold rules-based model (TF-RBM) which uses rules defined on the basis of sequential patterns mined from customer reviews. The first fold extracts aspects associated with domain independent opinions and the second fold extracts aspects associated with domain dependent opinions. We have also applied frequency- and similarity-based approaches to improve the aspect extraction accuracy of the proposed model. Our experimental evaluation has shown better results as compared with the state-of-the-art and most recent approaches. <s> BIB003 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> A. CROSS-DOMAIN SENTIMENT ANALYSIS <s> Abstract Aspect-Based Sentiment Analysis (ABSA) is a promising approach to analyze consumer reviews at a high level of detail, where the opinion about each feature of the product or service is considered. ABSA usually explores supervised inductive learning algorithms, which requires intense human effort for the labeling process. In this paper, we investigate Cross-Domain Transfer Learning approaches, in which aspects already labeled in some domains can be used to support the aspect extraction of another domain where there are no labeled aspects. Existing cross-domain transfer learning approaches learn classifiers from labeled aspects in the source domain and then apply these classifiers in the target domain, i.e., two separate stages that may cause inconsistency due to different feature spaces. To overcome this drawback, we present an innovative approach called CD-ALPHN (Cross-Domain Aspect Label Propagation through Heterogeneous Networks). First, we propose a heterogeneous network-based representation that combines different features (labeled aspects, unlabeled aspects, and linguistic features) from source and target domain as nodes in a single network. Second, we propose a label propagation algorithm for aspect extraction from heterogeneous networks, where the linguistic features are used as a bridge for this propagation. Our algorithm is based on a transductive learning process, where we explore both labeled and unlabeled aspects during the label propagation. Experimental results show that the CD-ALPHN outperforms the state-of-the-art methods in scenarios where there is a high-level of inconsistency between the source and target domains — the most common scenario in real-world applications. <s> BIB004
Network text has the characteristics of diversity and multidomain, so people hope to apply sentiment analysis algorithms to further exploration of text in different domains. Sentiment analysis has certain limitations on its corpus, and it is difficult for texts in different domains to train sentiment analysis models through the same corpus. For example, when the model trained by the Film Review Corpus is used to analyze the emotion of restaurant reviews, the performance of the model tends to be poor due to excessive differences between the two domains, and the restaurant evaluation cannot be analyzed accurately. Based on this, we must use specific corpus according to different text fields such as film reviews and restaurant reviews, but in reality, there are not enough data in many fields to support scholars' research on sentiment analysis, so cross-domain sentiment analysis has gradually become a hot topic of sentiment analysis. Cross-domain transfer learning aims to use source data from other domains to help target learning tasks BIB001 , which is used by more scholars in cross-domain sentiment analysis. Different domains of data have different feature spaces and data distributions. In this case, the research purpose is to extract the features that are useful in both target and source domains, and to obtain the shared feature space of cross-domain transfer learning problem. Some scholars select the best features of the source and target domains and reweight them to improve the final classification BIB002 . This process can be described as two phases. The first of which is to extract the common features of the two domains, and then the classifier is trained according to the useful features of the target domain BIB003 . However, the two-stage operation may be inconsistent due to the difference in feature space. To overcome this shortcoming, Maracini et al. BIB004 proposed CD-ALMA (Cross-Domain Aspect Label Propagation through Heterogeneous Networks), which has been proved to have a great effect by using heterogeneous networkbased representations to combine different features into nodes in the network and then exploring language representations through tag propagation algorithms for heterogeneous networks. However, cross-domain transfer learning still has certain problems, such as the selection of source domain, the noise interference in the combination of source domain and target domain, the optimization of the combination of source domain and target domain combined with feature space, etc., which brings challenges to cross-domain sentiment analysis.