Papers with aspect-level sentiment analysis
Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis (2020.aacl-main)
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| Challenge: | Recent studies ignored the syntactic relationship between the aspect and its corresponding context words, leading the model to focus on syntaktically unrelated words mistakenly. |
| Approach: | They propose to extend the graph convolutional network by assigning different weights to edges of connected words. |
| Outcome: | The proposed method can improve on five datasets showing that it learns and exploits multiword relations and draws different weights of words to improve performance. |
Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis (P19-2)
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| Challenge: | End-to-end deep learning systems lack flexibility as one cannot adjust the network to fix an obvious problem. |
| Approach: | They propose a way to leverage lexicon information to make the model more flexible . they also explore the effect of regularizing attention vectors to allow the network to have a broader "focus" |
| Outcome: | The proposed approach leverages lexicon information to make it more flexible and robust. |
Learning Sentence Representations over Tree Structures for Target-Dependent Classification (N18-1)
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| Challenge: | Existing work on tree structures uses syntactic parsers or Treebank annotations to perform target-dependent classifications. |
| Approach: | They propose a reinforcement learning based approach which automatically induces target-specific sentence representations over tree structures. |
| Outcome: | The proposed model gives superior performance on two benchmark tasks compared to previous work on parsed trees . |
Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain (2020.coling-main)
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| Challenge: | Existing datasets in automotive domain cover only three languages due to high cost of human annotation. |
| Approach: | They build large-scale datasets of users’ comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain. |
| Outcome: | The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datasets. |
Enhancing Aspect-level Sentiment Analysis with Word Dependencies (2021.eacl-main)
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| Challenge: | Existing approaches to enhance aspect-level sentiment analysis have omitted syntactic information . experimental results show that our approach outperforms baseline models on all datasets . |
| Approach: | They propose to leverage word dependencies to enhance aspect-level sentiment analysis . they propose to use key-value memory networks to leverage different dependency results . |
| Outcome: | The proposed approach outperforms baseline models on all datasets and achieves state-of-the-art performance on three of them. |
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection (2021.emnlp-main)
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| Challenge: | Existing studies on aspect-level sentiment analysis focus on extracting aspect terms and sentiment polarities separately. |
| Approach: | They propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-dimensional aspect-level sentiment analysis. |
| Outcome: | The proposed approach can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects. |
Siamese Network-Based Supervised Topic Modeling (D18-1)
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| Challenge: | Label-specific topics are widely used for supporting personality psychology, aspectlevel sentiment analysis, and crossdomain sentiment classification. |
| Approach: | They propose a supervised topic model based on the Siamese network which trades off label-specific word distributions with document-specific label distributions in a uniform framework. |
| Outcome: | The proposed model can trade off label-specific word distributions with document-specific label distributions in a uniform framework. |
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation (2022.emnlp-main)
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| Challenge: | Recent work on aspect sentiment quad prediction (ASQP) uses a template to extract aspect quadruplets from review sentences. |
| Approach: | They propose to use a pre-trained language model to select proper orders from a template order perspective to improve aspect sentiment quad prediction. |
| Outcome: | The proposed method outperforms state-of-the-art methods significantly in low-resource settings. |