Papers with sentiment prediction
From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation (2020.coling-main)
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| Challenge: | Existing sentiment analysis models lack temporal information to capture semantics of long texts. |
| Approach: | They propose a framework to exploit task-related discourse structures for sentiment analysis. |
| Outcome: | The proposed framework improves the performance even beyond existing approaches based on human annotated data. |
Knowledge-Enriched Two-Layered Attention Network for Sentiment Analysis (N18-2)
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| Challenge: | Existing sentiment analysis systems are prone to word shortening, exaggeration, lack of grammar and appropriate punctuation. |
| Approach: | They propose a two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis using the Knowledge Graph Embedding generated using the WordNet. |
| Outcome: | The proposed model outperforms the state-of-the-art system on the benchmark dataset of SemEval 2017 Task 5 by 1.7 and 3.7 points respectively. |
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification (P18-2)
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| Challenge: | Existing sentiment classification approaches do not fully exploit sentiment linguistic knowledge. |
| Approach: | They propose a Multi-sentiment-resource Enhanced Attention Network to integrate sentiment linguistic knowledge into the deep neural network via attention mechanisms. |
| Outcome: | The proposed network captures sentiments from different representation sub-spaces, and is superior to strong competitors. |
Learning Cooperative Interactions for Multi-Overlap Aspect Sentiment Triplet Extraction (2022.findings-emnlp)
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| Challenge: | Existing methods for aspect sentiment triplet extraction focus on the single interactions between an aspect and an opinion. |
| Approach: | They propose a multi-overlap triplet extraction method which decodes the complex relations between multiple aspects and opinions by learning their cooperative interactions. |
| Outcome: | The proposed method outperforms baselines, especially multi-overlap triplets. |
Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020.acl-main)
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| Challenge: | Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. |
| Approach: | They propose a relational graph attention network to encode a tree structure for sentiment prediction. |
| Outcome: | The proposed approach improves the performance of the graph attention network (GAT) on the SemEval 2014 and Twitter datasets. |
Modal Feature Optimization Network with Prompt for Multimodal Sentiment Analysis (2025.coling-main)
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| Challenge: | Multimodal sentiment analysis(MSA) is used to understand human emotional states through multimodal. |
| Approach: | They propose a Modal Feature Optimization Network with a modal prompt attention mechanism to optimize the under-optimized modal representation by determining which modalities are under- optimized . |
| Outcome: | The proposed method outperforms existing state-of-the-art models on public benchmark datasets. |
Visual Elements Mining as Prompts for Instruction Learning for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)
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| Challenge: | VEMP uses visual elements with text symbols embedded in the image to classify sentiment polarity towards a given opinion target. |
| Approach: | They propose a visual element mining as prompts method to fuse visual and text semantic information into instruction prompts for TMSC. |
| Outcome: | The proposed method achieves state-of-the-art performance on two benchmark datasets. |
Improving Document-Level Sentiment Analysis with User and Product Context (2020.coling-main)
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| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
| Approach: | They propose to incorporate all available historical review text belonging to the author of the review in question and investigate the inclusion of his- torical reviews associated with the current product. |
| Outcome: | The proposed model improves on IMDB, Yelp 2013 and Yelpan 2014 datasets by more than 2 percentage points in the best case. |
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)
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| Challenge: | Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention. |
| Approach: | They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task. |
| Outcome: | The proposed framework outperforms state-of-the-art on two public datasets. |
TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis (2025.findings-emnlp)
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| Challenge: | Existing Transformer-based methods with missing modalities are difficult to use and have quadratic complexity. |
| Approach: | They propose a text-enhanced Fusion Mamba framework for robust MSA with missing modalities . a Text-aware Modality Enhancement module aligns and enriches non-text modality while reconstructing missing text semantics. |
| Outcome: | The proposed method is efficient under missing modalities and can be used in long-range modeling and multimodal fusion scenarios. |
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis (2022.lrec-1)
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| Challenge: | Existing ABSA models do not pay attention to aspect terms and their contexts . a discriminator is introduced to improve ABSA, allowing for better understanding of aspect terms . |
| Approach: | They propose to improve ABSA by complementary learning of aspect terms . they explicitly recover aspect terms from each input sentence to better understand aspects . |
| Outcome: | The proposed approach improves ABSA on five widely used English benchmark datasets. |
Uncovering Sentiment Analysis Circuit in Large Language Model (2026.acl-long)
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| Challenge: | Prior work has shown that sentiment is encoded linearly in LLM representations, but their ability to utilize this information remains fragile to prompt variations. |
| Approach: | They propose a simple inference-time intervention method that amplifies circuit features to compensate for insufficient activation. |
| Outcome: | The proposed method improves on a sentiment analysis circuit with sparse autoencoders and circuit-level analysis. |