Related references
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Article
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Bo Huang et al.
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Summary: In recent years, aspect category detection has gained popularity due to the increase in customer reviews data on e-commerce and online platforms. This task involves categorizing reviews based on product features or entity aspects. Various methods, including supervised and unsupervised learning, have been proposed to tackle this problem. This article provides an overview of datasets, explores the methods used for aspect category detection, analyzes their strengths and weaknesses, and discusses challenges and future research directions.
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Lvxiaowei Xu et al.
Summary: This study proposes a hybrid graph convolutional network (HGCN) that synthesizes information from constituency tree and dependency tree to learn structural text regions related to specific targets and predict sentiment polarity. Experimental results show that the proposed method outperforms current state-of-the-art baselines on five public datasets.
Review
Computer Science, Artificial Intelligence
Lai Po Hung et al.
Summary: Sentiment Analysis is a well-known area in text mining, but recently more areas of text classification, such as emotion detection, are being explored. Emotion detection is valuable for social behavior analysis and decision making. It can support tasks such as opinion mining and early depression detection. This review focuses on text-based sentiment analysis and emotion detection, summarizing methods and examining models of emotion classes.
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Haiyan Wu et al.
Summary: Aspect-based sentiment analysis (ABSA) aims to extract sentiment-target pairs in review sentences. Previous methods based on recurrent neural networks (RNNs) struggle with accurately capturing sentiment pairs. Recent research incorporates dependency information into structured models, achieving better results, while ignoring domain knowledge related to entities in the comments. This paper proposes a Knowledge-aware Dependency Graph Network (KDGN) that incorporates domain knowledge, dependency labels, and syntax path, showing significant improvement on the ABSA task.
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Article
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Kyeonghun Kim et al.
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Huyen Trang Phan et al.
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Tianlin Zhang et al.
Summary: Mental illnesses are a global public health problem that negatively impact people's lives and society's health. With the rise of social media, there has been increasing research interest in using user-generated posts to detect mental illness. This article provides a comprehensive survey of approaches that incorporate emotion fusion in the detection of mental illness in social media. It reviews different fusion strategies, discusses challenges faced by researchers in this area, and suggests potential directions for future research.
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Article
Computer Science, Artificial Intelligence
Rui Mao et al.
Summary: With the breakthrough of large-scale pre-trained language model (PLM) technology, prompt-based classification tasks, such as sentiment analysis and emotion detection, have gained increasing attention. This study conducts a systematic empirical study on prompt-based sentiment analysis and emotion detection to investigate the biases of PLMs in affective computing.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
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Jacqueline Kazmaier et al.
Summary: This paper examines the use of ensemble models in sentiment classification, introducing several techniques for constructing heterogeneous ensembles and evaluating their performance. The results demonstrate significant performance improvements of several ensemble configurations compared to the best individual model across different data sets, identifying clear trends that may be valuable to other researchers in the field. Additionally, a novel ensemble selection approach is proposed to address storage and retraining challenges commonly associated with similar methods.
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Summary: Transformer-based pretrained language models have revolutionized natural language processing in the biomedical research community. This survey comprehensively explores the core concepts, taxonomy, challenges, and possible solutions of these models, providing valuable insights for further advancements.
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Bin Liang et al.
Summary: This paper proposes a graph convolutional network model Sentic GCN based on SenticNet to enhance the affective dependencies of sentences for aspect-based sentiment analysis. By integrating emotional knowledge from SenticNet, the model effectively handles contextual affective information in sentences, improving the effectiveness of sentiment polarity detection towards specific aspects.
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Manju Venugopalan et al.
Summary: Aspect level sentiment analysis is a fine-grained task that extracts aspects and their sentiment polarity from opinionated text. This research proposes an unsupervised model that uses minimal aspect seed words to guide the extraction process and enhance the performance. The model incorporates guided inputs, multiple pruning strategies, and semantic filters to improve performance. Evaluation results show competitive and appreciable performance on restaurant domain datasets.
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Tao Yang et al.
Summary: In this study, a new approach for aspect-based sentiment analysis is proposed by considering contextual, lexical, and syntactic cues. Experimental results demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
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Shi Feng et al.
Summary: Aspect-based sentiment analysis is a fine-grained task that detects the sentiment polarities of aspect words in a sentence. Current ABSA models primarily use graph-based methods but may rely excessively on the quality of dependency trees and overlook global sentence information. To address these issues, we propose a new ABSA model AG-VSR, which utilizes A2GR and VSR for final classification.
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Review
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D. U. Vidanagama et al.
Summary: A majority of customers and manufacturers who engage in e-commerce rely on reviews for making purchasing decisions. However, fake reviews are problematic as they mislead decision-making. This research proposes a method for detecting fake reviews by integrating linguistic features, Part-of-Speech (POS) features, and sentiment analysis features. The detection is improved through the use of a rule-based classifier and a domain feature ontology. The performance measures of the classifier are enhanced by considering the features together rather than separately.
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Summary: Researchers propose a knowledgeable prompt learning (KPL) method for fake news detection. By designing prompt templates and verbal words, and incorporating external knowledge, the KPL method outperforms traditional fine-tuning methods in fake news detection.
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Qiang Lu et al.
Summary: This paper proposes a novel graph convolutional network with sentiment interaction and multi-graph perception for aspect-based sentiment analysis. The model considers the semantic correlations within aspect phrases and the sentiment interaction relations between different aspects of a sentence. It generates different types of adjacency graphs and uses graph convolutional neural networks and a multi-graph perception mechanism to enrich dependencies and enhance context-awareness. Experimental results show that the proposed model outperforms state-of-the-art methods in terms of accuracy and macro-F1 score.
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PROCEEDINGS OF THE VLDB ENDOWMENT
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Bin Li et al.
Summary: This paper presents our proposed method for the PER and IRI shared tasks at WASSA 2022. We utilize prompt-based learning with a pre-trained language model to enhance the models, and employ data augmentation and model ensemble for improved results. Additionally, we provide an online software demonstration and the corresponding code for further research.
Proceedings Paper
Computer Science, Artificial Intelligence
Abubakar Abid et al.
Summary: Research found that the GPT-3 model exhibits anti-Muslim bias, which is varied and severe, and can be partially alleviated with positive text prompts.
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