Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis (N18-2)
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| Challenge: | Recent work on target-dependent biLSTMs has shown that they are ineffective in aspect-based sentiment analysis. |
| Approach: | They propose a novel architecture that uses external memory chains with a delayed memory update mechanism to track entities. |
| Outcome: | The proposed model improves on a TABSA task using external memory chains with a delayed memory update mechanism. |
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| Challenge: | Aspect-based sentiment analysis can provide more detailed information than general sentiment analysis. |
| Approach: | They propose a model based on convolutional neural networks and gating mechanisms which can selectively output the sentiment features according to the given aspect or entity. |
| Outcome: | The proposed model can selectively output sentiment features according to the given aspect or entity. |
Context-aware Embedding for Targeted Aspect-based Sentiment Analysis (P19-1)
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| Challenge: | Existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. |
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Target-Sensitive Memory Networks for Aspect Sentiment Classification (P18-1)
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| Challenge: | Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. |
| Approach: | They propose to use memory networks to deal with ASC using aspect and sentence terms and use them to classify the sentiment polarity. |
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Inducing Target-Specific Latent Structures for Aspect Sentiment Classification (2020.emnlp-main)
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| Challenge: | Aspect-level sentiment analysis aims to classify the sentiment polarity of an aspect or a target in a comment . graph convolutional networks can be used to classifice aspect terms in syllables . |
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Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)
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| Challenge: | Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
| Approach: | They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner. |
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Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis (N18-2)
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Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, Roger Zimmermann
| Challenge: | Present neural-based models exploit aspect and its contextual information in the sentence but ignore inter-aspect dependencies. |
| Approach: | They propose to combine aspect-based sentiment analysis with temporal dependency processing to incorporate this pattern into a sentence. |
| Outcome: | The proposed approach is based on the SemEval 2014 dataset and shows that it is effective for predicting sentiments of aspects in sentences with multiple aspects. |
Aspect-based Sentiment Analysis as Machine Reading Comprehension (2022.coling-1)
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| Challenge: | Existing approaches to aspect-based sentiment analysis stack multiple modules and result in severe error propagation. |
| Approach: | They propose a MRC-PrOmpt mOdeL framework where multiple sentiment aspects are elicited by a machine reading comprehension model and their corresponding sentiment polarities are classified in a prompt learning way. |
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Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification (D18-1)
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| Challenge: | Existing sentiment lexicons do not handle word sense and the concept of semantic compositionality is non-existent in simple lexiconic approaches. |
| Approach: | They propose a lexicon-driven contextual attention mechanism and a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. |
| Outcome: | The proposed model outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets. |
Exploiting Careful Design of SVM Solution for Aspect-term Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Aspect-term sentiment analysis (ATSA) identifies fine-grained sentiments towards specific aspects of text. |
| Approach: | They propose a pipeline to predict fine-grained sentiments for specific aspects of text . it decomposes the learning problem into multiple view subproblems and dynamically selects and constructs features with reinforcement learning. |
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IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis (D18-1)
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| Challenge: | Aspect-based sentiment analysis is a new approach to extract aspect specific sentimental information from user feedback. |
| Approach: | They propose a method that incorporates neighboring aspects related information into the sentiment classification of a target aspect using memory networks. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.6% on average in restaurant and laptop domains. |