Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition

• URL: http://arxiv.org/abs/2209.02276v1
• Date: Tue, 6 Sep 2022 08:02:55 GMT
• Title: Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition
• Authors: Pengfei Deng, Jianhua Yuan, Yanyan Zhao, Bing Qin
• Abstract summary: In this work, we achieve zero-shot aspect-level sentiment classification by only using document-level reviews. Our key intuition is that the sentiment representation of a document is composed of the sentiment representations of all the aspects of that document. AF-DSC first learns sentiment representations for all potential aspects and then aggregates aspect-level sentiments into a document-level one to perform document-level sentiment classification.
• Score: 13.955534334487618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As aspect-level sentiment labels are expensive and labor-intensive to acquire, zero-shot aspect-level sentiment classification is proposed to learn classifiers applicable to new domains without using any annotated aspect-level data. In contrast, document-level sentiment data with ratings are more easily accessible. In this work, we achieve zero-shot aspect-level sentiment classification by only using document-level reviews. Our key intuition is that the sentiment representation of a document is composed of the sentiment representations of all the aspects of that document. Based on this, we propose the AF-DSC method to explicitly model such sentiment composition in reviews. AF-DSC first learns sentiment representations for all potential aspects and then aggregates aspect-level sentiments into a document-level one to perform document-level sentiment classification. In this way, we obtain the aspect-level sentiment classifier as the by-product of the document-level sentiment classifier. Experimental results on aspect-level sentiment classification benchmarks demonstrate the effectiveness of explicit utilization of sentiment composition in document-level sentiment classification. Our model with only 30k training data outperforms previous work utilizing millions of data.

Related papers

• Contextual Document Embeddings [77.22328616983417]
  We propose two complementary methods for contextualized document embeddings. First, an alternative contrastive learning objective that explicitly incorporates the document neighbors into the intra-batch contextual loss. Second, a new contextual architecture that explicitly encodes neighbor document information into the encoded representation.
  arXiv  Detail & Related papers  (2024-10-03T14:33:34Z)
• 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)
• Out-of-Category Document Identification Using Target-Category Names as Weak Supervision [64.671654559798]
  Out-of-category detection aims to distinguish documents according to their semantic relevance to the inlier (or target) categories. We present an out-of-category detection framework, which effectively measures how confidently each document belongs to one of the target categories.
  arXiv  Detail & Related papers  (2021-11-24T21:01:25Z)
• Improving Document-Level Sentiment Classification Using Importance of Sentences [3.007949058551534]
  We propose a document-level sentence classification model based on deep neural networks. We conduct experiments using the sentiment datasets in the four different domains such as movie reviews, hotel reviews, restaurant reviews, and music reviews. The experimental results show that the importance of sentences should be considered in a document-level sentiment classification task.
  arXiv  Detail & Related papers  (2021-03-09T01:29:08Z)
• Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation [81.55533657694016]
  We propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation. Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three) We are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.
  arXiv  Detail & Related papers  (2020-11-02T08:07:50Z)
• 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)
• Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis [37.20068256769269]
  Document-level Sentiment Analysis (DSA) is more challenging due to vague semantic links and complicate sentiment information. We study how to effectively generate a discriminative representation with explicit subject patterns and sentiment contexts for DSA. We design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation.
  arXiv  Detail & Related papers  (2020-07-16T16:27:38Z)
• A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss [51.448615489097236]
  Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. We propose a novel dual-view model that jointly improves the performance of these two tasks. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.
  arXiv  Detail & Related papers  (2020-06-02T13:34:11Z)
• A Systematic Comparison of Architectures for Document-Level Sentiment Classification [14.670220716382515]
  We compare hierarchical models and transfer learning for document-level sentiment classification. We show that non-trivial hierarchical models outperform previous baselines and transfer learning on document-level sentiment classification in five languages.
  arXiv  Detail & Related papers  (2020-02-19T12:22:46Z)

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.