Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans
- URL: http://arxiv.org/abs/2010.02696v2
- Date: Mon, 12 Apr 2021 10:26:54 GMT
- Title: Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans
- Authors: Lu Xu, Lidong Bing, Wei Lu and Fei Huang
- Abstract summary: We present a neat and effective structured attention model by aggregating multiple linear-chain CRFs.
Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features.
- Score: 66.77264982885086
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Aspect based sentiment analysis, predicting sentiment polarity of given
aspects, has drawn extensive attention. Previous attention-based models
emphasize using aspect semantics to help extract opinion features for
classification. However, these works are either not able to capture opinion
spans as a whole, or not able to capture variable-length opinion spans. In this
paper, we present a neat and effective structured attention model by
aggregating multiple linear-chain CRFs. Such a design allows the model to
extract aspect-specific opinion spans and then evaluate sentiment polarity by
exploiting the extracted opinion features. The experimental results on four
datasets demonstrate the effectiveness of the proposed model, and our analysis
demonstrates that our model can capture aspect-specific opinion spans.
Related papers
- What Do Deep Saliency Models Learn about Visual Attention? [28.023464783469738]
We present a novel analytic framework that sheds light on the implicit features learned by saliency models.
Our approach decomposes these implicit features into interpretable bases that are explicitly aligned with semantic attributes.
arXiv Detail & Related papers (2023-10-14T23:15:57Z) - Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment
Classification [14.212306015270208]
We propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect.
In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect.
Our model achieves state-of-the-art results on three benchmark datasets.
arXiv Detail & Related papers (2023-08-22T13:55:36Z) - A Dual-Perspective Approach to Evaluating Feature Attribution Methods [40.73602126894125]
We propose two new perspectives within the faithfulness paradigm that reveal intuitive properties: soundness and completeness.
Soundness assesses the degree to which attributed features are truly predictive features, while completeness examines how well the resulting attribution reveals all the predictive features.
We apply these metrics to mainstream attribution methods, offering a novel lens through which to analyze and compare feature attribution methods.
arXiv Detail & Related papers (2023-08-17T12:41:04Z) - Joint Forecasting of Panoptic Segmentations with Difference Attention [72.03470153917189]
We study a new panoptic segmentation forecasting model that jointly forecasts all object instances in a scene.
We evaluate the proposed model on the Cityscapes and AIODrive datasets.
arXiv Detail & Related papers (2022-04-14T17:59:32Z) - Aspect-Controllable Opinion Summarization [58.5308638148329]
We propose an approach that allows the generation of customized summaries based on aspect queries.
Using a review corpus, we create a synthetic training dataset of (review, summary) pairs enriched with aspect controllers.
We fine-tune a pretrained model using our synthetic dataset and generate aspect-specific summaries by modifying the aspect controllers.
arXiv Detail & Related papers (2021-09-07T16:09:17Z) - SparseBERT: Rethinking the Importance Analysis in Self-attention [107.68072039537311]
Transformer-based models are popular for natural language processing (NLP) tasks due to its powerful capacity.
Attention map visualization of a pre-trained model is one direct method for understanding self-attention mechanism.
We propose a Differentiable Attention Mask (DAM) algorithm, which can be also applied in guidance of SparseBERT design.
arXiv Detail & Related papers (2021-02-25T14:13:44Z) - 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) - Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
We establish a novel set of evaluation criteria for such feature based explanations by analysis.
We obtain new explanations that are loosely necessary and sufficient for a prediction.
We extend the explanation to extract the set of features that would move the current prediction to a target class.
arXiv Detail & Related papers (2020-05-31T05:52:05Z) - Aspect Term Extraction using Graph-based Semi-Supervised Learning [1.0499611180329804]
This paper proposes a graph-based semi-supervised learning approach for aspect term extraction.
Every identified token in the review document is classified as aspect or non-aspect term from a small set of labeled tokens.
The proposed work is further extended to determine the polarity of the opinion words associated with the identified aspect terms in review sentence.
arXiv Detail & Related papers (2020-02-20T13:11:02Z)
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.