A Survey of Sentiment Analysis Based on Transfer Learning <s> 3) FEATURE-REPRESENTATION-TRANSFER METHODS <s> Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For many NLP tasks, however, we are confronted with new domains in which labeled data is scarce or non-existent. In such cases, we seek to adapt existing models from a resource-rich source domain to a resource-poor target domain. We introduce structural correspondence learning to automatically induce correspondences among features from different domains. We test our technique on part of speech tagging and show performance gains for varying amounts of source and target training data, as well as improvements in target domain parsing accuracy using our improved tagger. <s> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> 3) FEATURE-REPRESENTATION-TRANSFER METHODS <s> Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of users publishing sentiment data (e.g., reviews, blogs). Although traditional classification algorithms can be used to train sentiment classifiers from manually labeled text data, the labeling work can be time-consuming and expensive. Meanwhile, users often use some different words when they express sentiment in different domains. If we directly apply a classifier trained in one domain to other domains, the performance will be very low due to the differences between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain. In this cross-domain sentiment classification setting, to bridge the gap between the domains, we propose a spectral feature alignment (SFA) algorithm to align domain-specific words from different domains into unified clusters, with the help of domain-independent words as a bridge. In this way, the clusters can be used to reduce the gap between domain-specific words of the two domains, which can be used to train sentiment classifiers in the target domain accurately. Compared to previous approaches, SFA can discover a robust representation for cross-domain data by fully exploiting the relationship between the domain-specific and domain-independent words via simultaneously co-clustering them in a common latent space. We perform extensive experiments on two real world datasets, and demonstrate that SFA significantly outperforms previous approaches to cross-domain sentiment classification. <s> BIB002
The condition of feature-representation-transfer method is that the source domain and the target domain have part crossover features. It is necessary to transform the data of the two domains into the same feature space through feature transformation, and then perform traditional machine learning. Because this method has lower requirements for similarity between the two domains, it is more widely used and performs well in various tasks of NLP. The classic algorithms and applications for sentiment analysis are introduced below. SCL BIB001 (Structure Correspondence Learning): SCL uses the pivot and non-pivot features to construct a correlation model in order to complete the transfer learning. The algorithm is very dependent on the quality of the latent space and the number of auxiliary learning tasks, which has significant limitations. SFA BIB002 (Spectral Feature Alignment): The algorithm uses domain-independent words as a bridge to construct common latent space of domain-specific words in different fields and reduces differences in different fields for transfer learning. SFA assumes that there are rich source domain data and some labeled target domain data, and the bilateral graph model is built by identifying domain-independent words. If the graph shows that the domain-specific words of the two domains are connected to a common domain-independent word, then there is a higher chance that the two domainspecific word features are aligned.