Aspect-category-based sentiment analysis (ACSA), which aims to identify aspect categories and predict their sentiments has been intensively studied due to its wide range of NLP applications. Most approaches mainly utilize intrasentential features. However, a review often includes multiple different aspect categories, and some of them do not explicitly appear in the review. Even in a sentence, there is more than one aspect category with its sentiments, and they are entangled intra-sentence, which makes the model fail to discriminately preserve all sentiment characteristics. In this paper, we propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) for ACSA tasks. Specifically, we explore coherence modeling to capture the contexts across the whole review and to help the implicit aspect and sentiment identification. To address the issue of multiple aspect categories and sentiment entanglement, we propose a hierarchical disentanglement module to extract distinct categories and sentiment features. Extensive experimental and visualization results show that our ECAN effectively decouples multiple categories and sentiments entangled in the coherence representations and achieves state-of-the-art (SOTA) performance. Our codes and data are available online: https://github.com/cuijin-23/ECAN.

Aspect-based sentiment analysis (ABSA) has been widely studied since the explosive growth of social networking services. However, the recognition of implicit sentiments that do not contain obvious opinion words remains less explored. In this paper, we propose aspect-category enhanced learning with a neural coherence model (ELCoM). It captures document-level coherence by using contrastive learning, and sentence-level by a hypergraph to mine opinions from explicit sentences to aid implicit sentiment classification. To address the issue of sentences with different sentiment polarities in the same category, we perform cross-category enhancement to offset the impact of anomalous nodes in the hypergraph and obtain sentence representations with enhanced aspect-category. Extensive experiments on benchmark datasets show that the ELCoM achieves state-of-the-art performance. Our source codes and data are released at https://github.com/cuijin-23/ELCoM.

The positive text reframing (PTR) task which generates a text giving a positive perspective with preserving the sense of the input text, has attracted considerable attention as one of the NLP applications. Due to the significant representation capability of the pre-trained language model (PLM), a beneficial baseline can be easily obtained by just fine-tuning the PLM. However, how to interpret a diversity of contexts to give a positive perspective is still an open problem. Especially, it is more serious when the size of the training data is limited. In this paper, we present a PTR framework, that learns representations where the meaning and style of text are structurally disentangled. The method utilizes pseudo-positive reframing datasets which are generated with two augmentation strategies. A simple but effective multi-task learning-based model is learned to fuse the generation capabilities from these datasets. Experimental results on Positive Psychology Frames (PPF) dataset, show that our approach outperforms the baselines, BART by five and T5 by six evaluation metrics. Our source codes and data are available online.

Automatic Peer-review Aspect Score Prediction (PASP) of academic papers can be a helpful assistant tool for both reviewers and authors. Most existing works on PASP utilize supervised learning techniques. However, the limited number of peer-review data deteriorates the performance of PASP. This paper presents a novel semi-supervised learning (SSL) method that incorporates the Transformer fine-tuning into the Γ-model, a variant of the Ladder network, to leverage contextual features from unlabeled data. Backpropagation simultaneously minimizes the sum of supervised and unsupervised cost functions, avoiding the need for layer-wise pre-training. The experimental results show that our model outperforms the supervised and naive semi-supervised learning baselines. Our source codes are available online.

In the case of using a deep learning (machine learning) framework for emotion classification, one significant difficulty faced is the requirement of building a large, emotion corpus in which each sentence is assigned emotion labels. As a result, there is a high cost in terms of time and money associated with the construction of such a corpus. Therefore, this paper proposes a method of creating a semi-automatically constructed emotion corpus. For the purpose of this study sentences were mined from Twitter using some emotional seed words that were selected from a dictionary in which the emotion words were well-defined. Tweets were retrieved by one emotional seed word, and the retrieved sentences were assigned emotion labels based on the emotion category of the seed word. It was evident from the findings that the deep learning-based emotion classification model could not achieve high levels of accuracy in emotion classification because the semi-automatically constructed corpus had many errors when assigning emotion labels. In this paper, therefore, an approach for improving the quality of the emotion labels by automatically correcting the errors of emotion labels is proposed and tested. The experimental results showed that the proposed method worked well, and the classification accuracy rate was improved to 55.1% from 44.9% on the Twitter emotion classification task.