Sentence Constituent-Aware Aspect-Category Sentiment Analysis with Graph Attention Networks

• URL: http://arxiv.org/abs/2010.01461v1
• Date: Sun, 4 Oct 2020 01:23:17 GMT
• Title: Sentence Constituent-Aware Aspect-Category Sentiment Analysis with Graph Attention Networks
• Authors: Yuncong Li, Cunxiang Yin and Sheng-hua Zhong
• Abstract summary: Aspect category sentiment analysis aims to predict the sentiment polarities of the aspect categories discussed in sentences. We propose a Sentence Constituent-Aware Network (SCAN) for aspect-category sentiment analysis.
• Score: 9.287196185066565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect category sentiment analysis (ACSA) aims to predict the sentiment polarities of the aspect categories discussed in sentences. Since a sentence usually discusses one or more aspect categories and expresses different sentiments toward them, various attention-based methods have been developed to allocate the appropriate sentiment words for the given aspect category and obtain promising results. However, most of these methods directly use the given aspect category to find the aspect category-related sentiment words, which may cause mismatching between the sentiment words and the aspect categories when an unrelated sentiment word is semantically meaningful for the given aspect category. To mitigate this problem, we propose a Sentence Constituent-Aware Network (SCAN) for aspect-category sentiment analysis. SCAN contains two graph attention modules and an interactive loss function. The graph attention modules generate representations of the nodes in sentence constituency parse trees for the aspect category detection (ACD) task and the ACSA task, respectively. ACD aims to detect aspect categories discussed in sentences and is a auxiliary task. For a given aspect category, the interactive loss function helps the ACD task to find the nodes which can predict the aspect category but can't predict other aspect categories. The sentiment words in the nodes then are used to predict the sentiment polarity of the aspect category by the ACSA task. The experimental results on five public datasets demonstrate the effectiveness of SCAN.

Related papers

• You Shall Know a Tool by the Traces it Leaves: The Predictability of Sentiment Analysis Tools [74.98850427240464]
  We show that sentiment analysis tools disagree on the same dataset. We show that the sentiment tool used for sentiment annotation can even be predicted from its outcome.
  arXiv  Detail & Related papers  (2024-10-18T17:27:38Z)
• Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions [35.20091752343433]
  This work introduces two hierarchical contexts, namely perceptual context and spurious context, to carefully describe the precise category boundary. The two contexts hierarchically construct the precise description for a certain category, which is first roughly classifying a sample to the predicted category. The precise descriptions for those categories within the vision-language framework present a novel application: CATegory-EXtensible OOD detection (CATEX)
  arXiv  Detail & Related papers  (2024-07-23T12:53:38Z)
• Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis [12.024076910894417]
  We propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) for ACSA tasks. Our ECAN effectively decouples multiple categories and sentiments entangled in the coherence representations and achieves state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2024-03-15T11:32:44Z)
• AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation [33.25304533086283]
  Open-vocabulary semantic segmentation is a challenging task that requires segmenting novel object categories at inference time. Recent studies have explored vision-language pre-training to handle this task, but suffer from unrealistic assumptions in practical scenarios. This work proposes a novel attribute decomposition-aggregation framework, AttrSeg, inspired by human cognition in understanding new concepts.
  arXiv  Detail & Related papers  (2023-08-31T19:34:09Z)
• Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis [64.70116276295609]
  SentiWSP is a Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks. SentiWSP achieves new state-of-the-art performance on various sentence-level and aspect-level sentiment classification benchmarks.
  arXiv  Detail & Related papers  (2022-10-18T12:25:29Z)
• Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction [47.09579655541417]
  ALSC aims at identifying the sentiment polarity of a specified aspect in a sentence. Recent works fine-tune pre-trained Transformer encoders for ALSC to extract an aspect-centric dependency tree. We propose a span-based anti-bias aspect representation learning framework.
  arXiv  Detail & Related papers  (2021-09-06T12:32:42Z)
• Category Contrast for Unsupervised Domain Adaptation in Visual Tasks [92.9990560760593]
  We propose a novel Category Contrast technique (CaCo) that introduces semantic priors on top of instance discrimination for visual UDA tasks. CaCo is complementary to existing UDA methods and generalizable to other learning setups such as semi-supervised learning, unsupervised model adaptation, etc.
  arXiv  Detail & Related papers  (2021-06-05T12:51:35Z)
• Towards Novel Target Discovery Through Open-Set Domain Adaptation [73.81537683043206]
  Open-set domain adaptation (OSDA) considers that the target domain contains samples from novel categories unobserved in external source domain. We propose a novel framework to accurately identify the seen categories in target domain, and effectively recover the semantic attributes for unseen categories.
  arXiv  Detail & Related papers  (2021-05-06T04:22:29Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis [8.378067521821045]
  We propose a Multi-Instance Multi-Label Learning Network for Aspect-Category sentiment analysis (AC-MIMLLN) AC-MIMLLN treats sentences as bags, words as instances, and the words indicating an aspect category as the key instances of the aspect category. Experimental results on three public datasets demonstrate the effectiveness of AC-MIMLLN.
  arXiv  Detail & Related papers  (2020-10-06T12:07:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.