A Survey of Sentiment Analysis Based on Transfer Learning <s> SS-FE [62] (Feature Ensemble Plus Example Selection): <s> In this paper, we present an efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions. Specifically, we propose to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain. We cast this learning task as a compressed sensing problem, where each binary classifier induced from multiple classes can be deemed as a measurement sensor. Based on the compressive sensing theory, the estimation error of the transformation matrix decreases with the increasing number of classifiers. Therefore, to guarantee reconstruction performance, we construct sufficiently many binary classifiers based on the error correcting output coding. Extensive experiments are conducted on both a toy dataset and three real-world datasets to verify the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy. <s> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> SS-FE [62] (Feature Ensemble Plus Example Selection): <s> Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of user generated sentiment data (e.g., reviews, blogs). In real applications, these users generated sentiment data can span so many different domains that it is difficult to manually label training data for all of them. In this article, we develop a general solution to cross-domain sentiment classification when we do not have any labeled data in a target domain but have some labeled data in a source domain. To bridge the gap between domains, we propose a novel algorithm, called topical correspondence transfer (TCT). This is achieved by learning the domain-specific information from different domains into unified topics, with the help of shared topics across all domains. In this way, the topical correspondences behind the shared topics can be used as a bridge to reduce the gap between domains. We conduct experiments on a benchmark composed of reviews of 4 types of Amazon products. Experimental results show that our proposed TCT significantly outperforms the baseline method, and achieves an accuracy which is competitive with the state-of-the-art methods for cross-domain sentiment classification. HighlightsWe propose a novel algorithm, called topical correspondence transfer (TCT) for cross-domain sentiment classification.The proposed framework is achieved by learning the domain-specific information from different domains into unified topics, with the help of shared topics across all domains.An objective function is defined to simultaneously learn a topical representation and document sentiment prediction with the model. <s> BIB002
A tag adaptation algorithm based on part-of-speech(POS) feature ensemble, which combines its proposed Feature Ensemble(FE) model with a Principal Component Analysis-based Sample Selection(PCA-SS) algorithm to achieve transfer learning. TCT BIB002 (topical correspondence transfer): To reduce the distribution difference by establishing the correspondence between other topics in different topics, and finally discover the topic and classify the emotions in the process of optimizing the objective function. The core element of TCT is the optimization problem, which is expressed as a joint nonnegative matrix factorization. When the features of the two domains are completely different, they belong to a heterogeneous environment. Zhou et al. BIB001 mentioned a hybrid heterogeneous transfer learning (HTTL) method to solve the sentiment classification of a heterogeneous environment with rich labeled source domain data and unlabeled target domain data.This method can simplify the problem to the isomorphic adaptation problem by learning the asymmetric transformation from the target domain to the source domain. The key to solving the isomorphic adaptation problem is to reduce the distribution deviation between the target domain and the labeled source domain of the transition, that is, to find a common potential feature space. Finally, the classifier of the method is jointly trained by a common potential feature space from the marked source domain data.