Papers with sentiment classification tasks
Domain Adapted Word Embeddings for Improved Sentiment Classification (P18-2)
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| Challenge: | Generic word embeddings are trained on large-scale generic corpora, while domain specific ones are trained only on data from a domain of interest. |
| Approach: | They propose a method to combine breadth of generic and specific embeddings to form domain-specific embeddables. |
| Outcome: | The proposed method outperforms generic and domain specific embeddings on sentiment classification tasks. |
Sentiment as an Ordinal Latent Variable (2023.eacl-main)
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| Challenge: | Existing dictionaries are limited in coverage and sentiment scales vary widely; some are discrete others continuous. |
| Approach: | They propose a Bayesian generative model that learns a composite sentiment dictionary as an interpolation between six existing dictionaries with different scales. |
| Outcome: | The proposed model learns a composite sentiment dictionary as an interpolation between six existing dictionaries with different scales. |
Investigating Political Herd Mentality: A Community Sentiment Based Approach (P19-2)
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| Challenge: | polarities inherent in political speeches and debates pose an important problem today. |
| Approach: | They propose to use community-based graphs to augment hand-crafted features based on topic modeling and emotion detection on debate transcripts. |
| Outcome: | The proposed approach surpasses the benchmark results on the same dataset. |
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification (2020.coling-main)
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| Challenge: | Existing deep learning paradigms focus on learning a model from training data of a single task and the learned model is also tested on the same task. |
| Approach: | They propose a Bayes-enhanced lifelong attention network to learn attention knowledge from a sequence of sentiment classification tasks and build lifelong ones. |
| Outcome: | The proposed model is able to learn attention knowledge from a set of sentiment classification tasks and build lifelong attentions. |
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis (C18-1)
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| Challenge: | Existing attention models do not take full advantage of sentiment lexicons, which provide rich sentiment information and play a critical role in sentiment analysis. |
| Approach: | They propose a lexicon-based supervised attention model which allows a neural network to focus on the sentiment content, thus generating sentiment-informative representations. |
| Outcome: | The proposed model outperforms existing models on three large-scale sentiment classification datasets. |
Using the Past Knowledge to Improve Sentiment Classification (2020.findings-emnlp)
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| Challenge: | Existing model retains knowledge learned from past tasks and selectively transfers it to new task to help it learn better. |
| Approach: | They propose a lifelong learning model that can retain and selectively transfer the knowledge learned in the past to help learn the new task. |
| Outcome: | The proposed model outperforms strong baselines, including even multiple task learning. |
Improving Formality Style Transfer with Context-Aware Rule Injection (2021.acl-long)
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| Challenge: | Existing language models pre-trained on large-scale corpora often do not work well for user-generated data where the language styles differ significantly from the mainstream text. |
| Approach: | They propose a method for formality style transfer by injecting multiple rules into an end-to-end BERT-based encoder and decoder model. |
| Outcome: | The proposed method outperforms existing rule-based FST approaches on tweet sentiment analysis tasks. |
ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger (2024.naacl-long)
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| Challenge: | Textual backdoor attacks are increasingly challenging to detect due to the use of advanced generative models such as GPT-4. |
| Approach: | They propose a framework that harnesses advanced generative models to execute stealthier backdoor attacks on text classifiers. |
| Outcome: | The proposed framework achieves state-of-the-art attack success rate of 97.35% over four sentiment classification tasks and four human cognition stealthiness tests. |
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. |
Diffusion Based Counterfactual Augmentation for Dual Sentiment Classification (2024.lrec-main)
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| Challenge: | Existing methods to augment sentiment models have failed to mitigate spurious association problem inherent in the original data. |
| Approach: | They propose a framework for enhancing sentiment models using an antonymous paradigm and contrastive learning to generate high-quality samples. |
| Outcome: | The proposed framework achieves state-of-the-art performance on four benchmark datasets. |
SMASH: Improving SMAll Language Models’ Few-SHot Ability with Prompt-Based Distillation (2022.findings-emnlp)
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| Challenge: | Large-scale language models with prompts have shown remarkable performance on few-shot learning. |
| Approach: | They propose an approach to improve SMAll language models’ few-SHot ability by training on intermediate tasks before prompt-based fine-tuning on downstream tasks. |
| Outcome: | The proposed model improves on sentence-pair and sentiment classification tasks by training on intermediate tasks before fine-tuning on downstream tasks. |
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning (2020.acl-main)
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| Challenge: | Recent neural network models have achieved impressive performance on sentiment classification in English and other languages. |
| Approach: | They propose an unsupervised sentiment classification model that leverages an uncontrolled machine translation system and a language discriminator to learn a shared representation. |
| Outcome: | The proposed model outperforms other models on five language pairs. |
One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification (2022.coling-1)
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| Challenge: | Existing knowledge distillation models require large computing resources and long inference time to perform. |
| Approach: | They propose a one-teacher and multiple-student knowledge distillation approach to distill a deep pre-trained teacher model into multiple shallow student models with ensemble learning. |
| Outcome: | The proposed method achieves better results with fewer parameters and extremely high speedup ratios on three sentiment classification tasks. |
PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training (2023.emnlp-main)
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| Challenge: | Existing methods for text classification use label names of target classes as the only supervision. |
| Approach: | They propose a method that uses keyword-based keyword matching to generate pseudo labels . they propose 'pieclass' module that iteratively trains classifiers and updates pseudo labels. |
| Outcome: | The proposed method achieves better performance than existing strong baselines on seven benchmark datasets and similar performance to fully-supervised classifiers on sentiment classification tasks. |