| Challenge: | Recent work on injecting sentiment knowledge into pre-trained language models, but it is difficult to integrate external knowledge into PLMs. |
| Approach: | They propose two sentiment-aware auxiliary tasks to integrate sentiment knowledge into the objective of the downstream task. |
| Outcome: | The proposed tasks outperform baselines and complement existing sentiment-enhanced models. |
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SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis (2020.acl-main)
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| Challenge: | sentiment knowledge is ignored in sentiment analysis, despite its use in pretraining. |
| Approach: | They propose to use sentiment knowledge to learn a unified sentiment representation for multiple sentiment analysis tasks. |
| Outcome: | The proposed method outperforms strong pre-training baseline on three kinds of sentiment tasks. |
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features (2022.coling-1)
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| Challenge: | Existing research results on explicit sentiment analysis are limited . implicit sentiment analysis is a process of analyzing text based on whether it contains explicit sentiment words. |
| Approach: | They propose a model that integrates external knowledge and contextual features . they use a knowledge graph to supplement implicit sentiment expression . |
| Outcome: | The proposed model can achieve better results on the SMP2019 implicit sentiment analysis dataset. |
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. |
| Outcome: | The proposed pipeline surpasses SVM-based methods in predictive accuracy while maintaining a faster inference speed and significantly reducing the number of model parameters. |
Sentiment Knowledge Enhanced Self-supervised Learning for Multimodal Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing studies have used general approaches to alleviate the overfitting of supervised models based on video data with sentiment annotations. |
| Approach: | They propose to capture common sentimental patterns in unlabeled videos using sentiment knowledge and non-verbal behavior to embed sentiment information into pre-trained multimodal representations. |
| Outcome: | The proposed model outperforms the baseline and achieves new State-Of-The-Art (SOTA) results. |
Encoding Sentiment Information into Word Vectors for Sentiment Analysis (C18-1)
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| Challenge: | Existing methods for embedding sentiment knowledge into word vectors are generally trained independently of the downstream task. |
| Approach: | They propose to encode sentiment knowledge into pre-trained word vectors to improve sentiment analysis. |
| Outcome: | The proposed method improves sentiment analysis on four popular sentiment datasets compared to benchmark methods. |
An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing approaches to Aspect-Based Sentiment Analysis (ABSA) are lacking in a comprehensive evaluation and fair comparison. |
| Approach: | They propose to use a knowledge-mining method to build a large-scale knowledge-annotated SPT corpus and integrate sentiment knowledge into pre-training. |
| Outcome: | The proposed method is able to build a large-scale knowledge-annotated SPT corpus and compares with other methods. |
Domain-Specific Sentiment Lexicons Induced from Labeled Documents (2020.coling-main)
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| Challenge: | Existing sentiment lexicons reflect abstract notion of polarity and do not do justice to substantial differences of word polarities between domains. |
| Approach: | They propose to use domain-specific sentiment lexicons to induce initial word intensity scores and train new deep models based on word vector representations to overcome the scarcity of the seed data. |
| Outcome: | The proposed models show that they perform well on review classification and cross-lingual word sentiment prediction. |
Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis (2021.emnlp-main)
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| Challenge: | Existing methods for data augmentation address data deficiencies and semantic consistency, but they ignore the second issue. |
| Approach: | They propose a semantics-preserving data augmentation approach that preserves the semantics of a textual sequence. |
| Outcome: | The proposed method achieves better performance on publicly available datasets and stock price/risk movement prediction scenarios. |
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs (N19-1)
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| Challenge: | Existing methods to categorize sentiments and emotions in text are limited. |
| Approach: | They propose to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. |
| Outcome: | The proposed method improves on a recently published dataset for categorizing human needs. |
Learning Sentiment Memories for Sentiment Modification without Parallel Data (D18-1)
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| Challenge: | Existing methods for sentiment modification generate input-irrelevant texts due to lack of parallel data. |
| Approach: | They propose a method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. |
| Outcome: | The proposed method significantly improves the content preservation degree and achieves the state-of-the-art performance. |