Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification (2020.emnlp-main)
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| Challenge: | Experimental results show that D-MILN outperforms recent weakly-supervised baselines . document-level multi-aspect sentiment classification requires a lot of manual aspect-level annotations - which is time-consuming and laborious . |
| Approach: | They propose a novel Diversified Multiple Instance Learning Network to achieve DMSC with only document-level weak supervision. |
| Outcome: | The proposed method outperforms weakly-supervised baselines on TripAdvisor and BeerAdvocate datasets. |
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| Challenge: | Existing methods to detect sentiment toward aspect categories ignore the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category in the sentence, which leads to suboptimal performance. |
| Approach: | They propose a multi-instance multi-label learning network for Aspect-Category sentiment analysis that treats sentences as bags, words as instances, and the words indicating an aspect category as key instances of the aspect category. |
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A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification (N19-1)
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| Challenge: | Existing weakly supervised methods for document-level multi-aspect sentiment classification are not easy to obtain. |
| Approach: | They propose a variational approach to weakly supervised document-level multi-aspect sentiment classification using target-opinion word pairs as "supervision" they aim to learn a sentiment polarity classifier by optimizing the lower bound . |
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Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification (D18-1)
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| Challenge: | Existing approaches to aspect level sentiment classification ignore aspect information, causing large error. |
| Approach: | They propose a parameterized convolutional neural network for aspect level sentiment classification . they incorporate aspect information into convolutionally-based neural networks . |
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An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis (P19-1)
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| Challenge: | Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a sentence. |
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Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings (C18-1)
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| Challenge: | Existing approaches focus on text information, but authors and overall ratings are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects. |
| Approach: | They propose a hierarchical user-aspect rating network model to consider user preference and overall ratings jointly. |
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Aspect Sentiment Classification with Document-level Sentiment Preference Modeling (2020.acl-main)
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| Challenge: | Existing studies consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect. |
| Approach: | They propose a Cooperative Graph Attention Networks approach for cooperatively learning aspect-related sentence representation. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods in document-level sentiment classification. |
Enhanced Aspect Level Sentiment Classification with Auxiliary Memory (C18-1)
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| Challenge: | Aspect level sentiment classification is a subtask of document or sentence level sentiment analysis. |
| Approach: | They propose a deep memory network with auxiliary memory to solve this problem . main memory is used to capture important context words for sentiment classification . auxiliary memories implicitly convert aspects and terms to each other . |
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Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning (2020.lrec-1)
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| Challenge: | Existing methods to label large datasets that resemble real life situations are prohibitive due to the cost of manual labeling. |
| Approach: | They propose to automate the annotation process by using end-to-end differentiable neural networks to label large datasets that resemble real life conditions. |
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Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning (D19-1)
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Jingjing Wang, Changlong Sun, Shoushan Li, Jiancheng Wang, Luo Si, Min Zhang, Xiaozhong Liu, Guodong Zhou
| Challenge: | Recent neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC) however, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. |
| Approach: | They propose a Hierarchical Reinforcement Learning approach to DASC that incorporates clause selection and word selection strategies to tackle the data noise problem. |
| Outcome: | The proposed approach over the state-of-the-art approaches shows impressive performance over the current baselines. |
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis (P19-1)
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| Challenge: | Experimental results show that our proposed approach yields better attention mechanisms . dominant ASC models are mostly discriminative classifiers based on manual feature engineering . |
| Approach: | They propose a self-supervised approach to aspect-level sentiment classification that mines useful attention supervision information from a training corpus to refine attention mechanisms. |
| Outcome: | The proposed approach yields better attention mechanisms on multiple datasets. |