A Survey of Sentiment Analysis Based on Transfer Learning <s> 2) INSTANCE-TRANSFER METHODS <s> Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model. <s> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> 2) INSTANCE-TRANSFER METHODS <s> This paper presents two instance-level transfer learning based algorithms for cross lingual opinion analysis by transferring useful translated opinion examples from other languages as the supplementary training data for improving the opinion classifier in target language. Starting from the union of small training data in target language and large translated examples in other languages, the Transfer AdaBoost algorithm is applied to iteratively reduce the influence of low quality translated examples. Alternatively, starting only from the training data in target language, the Transfer Self-training algorithm is designed to iteratively select high quality translated examples to enrich the training data set. These two algorithms are applied to sentence- and document-level cross lingual opinion analysis tasks, respectively. The evaluations show that these algorithms effectively improve the opinion analysis by exploiting small target language training data and large cross lingual training data. <s> BIB002 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> 2) INSTANCE-TRANSFER METHODS <s> This work proposes a boosting-based transfer learning approach for head-pose classification from multiple, low-resolution views. Head-pose classification performance is adversely affected when the source (training) and target (test) data arise from different distributions (due to change in face appearance, lighting, etc). Under such conditions, we employ Xferboost, a Logitboost-based transfer learning framework that integrates knowledge from a few labeled target samples with the source model to effectively minimize misclassifications on the target data. Experiments confirm that the Xferboost framework can improve classification performance by up to 6%, when knowledge is transferred between the CLEAR and FBK four-view headpose datasets. <s> BIB003 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> 2) INSTANCE-TRANSFER METHODS <s> We consider the characterization of muscle fatigue through a noninvasive sensing mechanism such as Surface ElectroMyoGraphy (SEMG). While changes in the properties of SEMG signals with respect to muscle fatigue have been reported in the literature, the large variation in these signals across different individuals makes the task of modeling and classification of SEMG signals challenging. Indeed, the variation in SEMG parameters from subject to subject creates differences in the data distribution. In this article, we propose two transfer learning frameworks based on the multisource domain adaptation methodology for detecting different stages of fatigue using SEMG signals, that addresses the distribution differences. In the proposed frameworks, the SEMG data of a subject represent a domain; data from multiple subjects in the training set form the multiple source domains and the test subject data form the target domain. SEMG signals are predominantly different in conditional probability distribution across subjects. The key feature of the first framework is a novel weighting scheme that addresses the conditional probability distribution differences across multiple domains (subjects) and the key feature of the second framework is a two-stage domain adaptation methodology which combines weighted data from multiple sources based on marginal probability differences (first stage) as well as conditional probability differences (second stage), with the target domain data. The weights for minimizing the marginal probability differences are estimated independently, while the weights for minimizing conditional probability differences are computed simultaneously by exploiting the potential interaction among multiple sources. We also provide a theoretical analysis on the generalization performance of the proposed multisource domain adaptation formulation using the weighted Rademacher complexity measure. We have validated the proposed frameworks on Surface ElectroMyoGram signals collected from 8 people during a fatigue-causing repetitive gripping activity. Comprehensive experiments on the SEMG dataset demonstrate that the proposed method improves the classification accuracy by 20p to 30p over the cases without any domain adaptation method and by 13p to 30p over existing state-of-the-art domain adaptation methods. <s> BIB004 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> 2) INSTANCE-TRANSFER METHODS <s> This paper describes a novel method to enhance underwater images by image dehazing. Scattering and color change are two major problems of distortion for underwater imaging. Scattering is caused by large suspended particles, such as turbid water which contains abundant particles. Color change or color distortion corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. Our key contributions are proposed a new underwater model to compensate the attenuation discrepancy along the propagation path, and proposed a fast joint trigonometric filtering dehazing algorithm. The enhanced images are characterized by reduced noised level, better exposedness of the dark regions, improved global contrast while the finest details and edges are enhanced significantly. In addition, our method is comparable to higher quality than the state-of-the-art methods by assuming in the latest image evaluation systems. <s> BIB005 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> 2) INSTANCE-TRANSFER METHODS <s> Transfer learning has been used as a machine learning method to make good use of available language resources for other resource-scarce languages. However, the cumulative class noise during iterations of transfer learning can lead to negative transfer which can adversely affect performance when more training data is used. In this paper, we propose a novel transfer learning method which can detect negative transfers. This approach detects high quality samples after certain iterations to identify class noise in new transferred training samples and remove them to reduce misclassifications. With the ability to detect bad training samples and remove them, our method can make full use of large unlabeled training data available in the target language. Furthermore, the most important contribution in this paper is the theory of class noise detection. Our new class noise detection method overcame the theoretic flaw of a previous method based on Gaussian distribution. We applied this transfer learning method with negative transfer detection to cross lingual opinion analysis. Evaluation on the NLP&CC 2013 cross-lingual opinion analysis dataset shows that the proposed approach outperforms the state-of-the-art systems. <s> BIB006
Instance-transfer method refers to the data sharing of the source and target domain. It can be filtered from source domain by re-weight. Through it, data from the target domain can be augmented with the labeled source domain samples. Several classic algorithms for transfer learning will be introduced at first. TrAdaBoost BIB001 is an algorithm for extracting instances from source data, which combines some of the available labeled source data with a small number of labeled target data to construct a more accurate model than simply using labeled target data. The algorithm works well when the source data and the target data have many similarities, but if the samples in the target data have more noise, it is possible to increase the difficulty of training the classifier. Vieriu et al. BIB003 proposed a boosting-based transfer learning method, which can transfer knowledge to different target areas in the training field by learning training samples from multiple fields, and reduce the cost of labeling target areas. Wang and Li  introduced a transfer learning method based on boosting framework and distributed measurement method. They believe that the TrAdaBoost algorithm forces the boosting mechanism to ignore valuable data while learning valuable data, but it is still not perfect. CP-MDA BIB004 (conditional probability based multi-source domain adaptation) can correct the difference in conditional distribution between the two domains. The core idea is to use the source domain classifier to tag unlabeled data in the target domain. The first step is to assign a classifier to the source domain, and then find a weight value for each classifier as a function of the proximity of the two domains in the conditional distribution between the two domains. The weighted combination source domain classifier creates a learning task by accumulating a pseudo tag for the unlabeled target domain data. Finally, the target learner is trained based on the tagged target domain data and the target domain data of the pseudo tag. 2SW-MDA BIB004 (two stage weighing framework for multisource domain adaptation), which is an upgraded version of CP-MDA, can handle the difference in edge distribution. The algorithm can calculate the weight for each source domain by the difference of the edge distribution between the two domains, and use the weight as a function of the difference of the conditional distribution obtained in the CP-MDA method. The innovation of the method is to calculate the weight of the source domain as a conditional probability function. Next, we will introduce examples on the application of transfer learning algorithms for sentiment analysis. Xu et al. BIB002 proposed the instance level transfer learning method applied to cross lingual opinion analysis, and translated other useful markup languages into target language as supplementary training data to improve the opinion classification in the target language. TrAdaBoost BIB001 algorithm is used to reduce the impact of low quality translation corpus. The algorithm effectively improves the current situation of opinion analysis of resource-scarce languages by using small target language training data and large cross lingual training data. Transfer learning can use the source domain data to augment the target domain data by the above method, but in the process of transfer, misclassification may occur to cause negative transfer, that is, the knowledge learned in the source domain has a negative effect on learning on the target domain. In response to the above problems, Gui et al. BIB006 proposed a negative transfer detection method for cross lingual analysis, which examines and removes the noise of the training samples by detecting high quality samples. This method can effectively reduce the misclassification by iterative means. Serikawa and Lu BIB005 introduced a fast joint trigonometric filtering dehazing algorithm that can detect and reduce the VOLUME 7, 2019 image noise, it can be used as a reference for noise detection of cross lingual analysis. Wang et al.  described an adaptive training data selection method, which can effectively avoid the noisy and fuzzy of source domain training data. The iterative method is used to combine the samples from the source domain and the target domain training data by using the informativeness measures, to evaluate the performance of the classifier of the target domain, and to update the informativeness measures of the next iteration.