Sentiment analysis of patient feedback from the public health domain can aid decision makers in evaluating the provided services. The current paper focuses on free-text comments in patient surveys about general practitioners and psychiatric healthcare, annotated with four sentence-level polarity classes -- positive, negative, mixed and neutral -- while also attempting to alleviate data scarcity by leveraging general-domain sources in the form of reviews. For several different architectures, we compare in-domain and out-of-domain effects, as well as the effects of training joint multi-domain models.

Sentiment Classification is a fundamental task in the field of Natural Language Processing, and has very important academic and commercial applications. It aims to automatically predict the degree of sentiment present in a text that contains opinions and subjectivity at some level, like product and movie reviews, or tweets. This can be really difficult to accomplish, in part, because different domains of text contains different words and expressions. In addition, this difficulty increases when text is written in a non-English language due to the lack of databases and resources. As a consequence, several cross-domain and cross-language techniques are often applied to this task in order to improve the results. In this work we perform a study on the ability of a classification system trained with a large database of product reviews to generalize to different Spanish domains. Reviews were collected from the MercadoLibre website from seven Latin American countries, allowing the creation of a large and balanced dataset. Results suggest that generalization across domains is feasible though very challenging when trained with these product reviews, and can be improved by pre-training and fine-tuning the classification model.

Cross-domain sentiment classification has been a hot spot these years, which aims to learn a reliable classifier using labeled data from the source domain and evaluate it on the target domain. In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space. To further improve the model performance, several methods targeted to mine domain-specific information were proposed. However, most of them only utilized a limited part of domain-specific information. In this study, we first develop a method of extracting domain-specific words based on the topic information. Then, we propose a Topic Driven Adaptive Network (TDAN) for cross-domain sentiment classification. The network consists of two sub-networks: semantics attention network and domain-specific word attention network, the structures of which are based on transformers. These sub-networks take different forms of input and their outputs are fused as the feature vector. Experiments validate the effectiveness of our TDAN on sentiment classification across domains.

Cross-domain sentiment classification aims to leverage useful information in a source domain to help do sentiment classification in a target domain that has no or little supervised information. Existing cross-domain sentiment classification methods cannot automatically capture non-pivots, i.e., the domain-specific sentiment words, and pivots, i.e., the domain-shared sentiment words, simultaneously. In order to solve this problem, we propose a Hierarchical Attention Transfer Network (HATN) for cross-domain sentiment classification. The proposed HATN provides a hierarchical attention transfer mechanism which can transfer attentions for emotions across domains by automatically capturing pivots and non-pivots. Besides, the hierarchy of the attention mechanism mirrors the hierarchical structure of documents, which can help locate the pivots and non-pivots better. The proposed HATN consists of two hierarchical attention networks, with one named P-net aiming to find the pivots and the other named NP-net aligning the non-pivots by using the pivots as a bridge. Specifically, P-net firstly conducts individual attention learning to provide positive and negative pivots for NP-net. Then, P-net and NP-net conduct joint attention learning such that the HATN can simultaneously capture pivots and non-pivots and realize transferring attentions for emotions across domains. Experiments on the Amazon review dataset demonstrate the effectiveness of HATN.

Cross-domain sentiment classification aims to tag sentiments for a target domain by labeled data from a source domain. Due to the difference between domains, the accuracy of a trained classifier may be very low. In this paper, we propose a boosting-based learning framework named TR-TrAdaBoost for cross-domain sentiment classification. We firstly explore the topic distribution of documents, and then combine it with the unigram TrAdaBoost. The topic distribution captures the domain information of documents, which is valuable for cross-domain sentiment classification. Experimental results indicate that TR-TrAdaBoost represents documents well and boost the performance and robustness of TrAdaBoost.

The domain adaptation problem arises often in the field of sentiment classification. There are two distinct needs in domain adaptation, namely labeling adaptation and instance adaptation. Most of current research focuses on the former one, while neglects the latter one. In this work, we propose a joint approach, named feature ensemble plus sample selection (SS-FE), which takes both types of adaptation into account. A feature ensemble (FE) model is first proposed to learn a new labeling function in a feature re-weighting manner. Furthermore, a PCA-based sample selection (PCA-SS) method is proposed as an aid to FE for instance adaptation. Experimental results show that the proposed SS-FE approach could gain significant improvements, compared to individual FE and PCA-SS, due to its comprehensive consideration of both labeling adaptation and instance adaptation.

Due to the explosive growth of the Internet online reviews, we can easily collect a large amount of labeled reviews from different domains. But only some of them are beneficial for training a desired target-domain sentiment classifier. Therefore, it is important for us to identify those samples that are the most relevant to the target domain and use them as training data. To address this problem, a novel approach, based on instance selection and instance weighting via PU learning, is proposed. PU learning is used at first to learn an in-target-domain selector, which assigns an in-target-domain probability to each sample in the training set. For instance selection, the samples with higher in-target-domain probability are used as training data; For instance weighting, the calibrated in-target-domain probabilities are used as sampling weights for training an instance-weighted naive Bayes model, based on the principle of maximum weighted likelihood estimation. The experimental results prove the necessity and effectiveness of the approach, especially when the size of training data is large. It is also proved that the larger the Kullback-Leibler divergence between the training and test data is, the more effective the proposed approach will be.

In the literature, various approaches have been proposedto address the domain adaptation problem in sentiment classification (also called cross-domainsentiment classification). However, the adaptation performance normally much suffers when the data distributionsin the source and target domains differ significantly. In this paper, we suggest to perform activelearning for cross-domain sentiment classification by actively selecting a smallamount of labeled data in the target domain. Accordingly, we propose an novel activelearning approach for cross-domain sentiment classification. First, we traintwo individual classifiers, i.e., the source and target classifiers with thelabeled data from the source and target respectively. Then, the two classifiersare employed to select informative samples with the selection strategy of QueryBy Committee (QBC). Third, the two classifier is combined to make theclassification decision. Importantly, the two classifiers are trained by fullyexploiting the unlabeled data in the target domain with the label propagation(LP) algorithm. Empirical studies demonstrate the effectiveness of our active learning approach for cross-domainsentiment classification over some strong baselines.