Papers Sentiment Classification
“Sentiment Classification” 태그가 달린 논문 949편 · 필터 해제
Multitask Learning for Low Resource Spoken Language Understanding
We explore the benefits that multitask learning offer to speech processing as we train models on dual objectives with automatic speech recognition and intent classification or sentiment classification. Our models, althou…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Classificationintent-classification+6Using Selective Masking as a Bridge between Pre-training and Fine-tuning
Pre-training a language model and then fine-tuning it for downstream tasks has demonstrated state-of-the-art results for various NLP tasks. Pre-training is usually independent of the downstream task, and previous works h…
Language ModellingSentiment AnalysisSentiment ClassificationSentiment Classification of Code-Switched Text using Pre-trained Multilingual Embeddings and Segmentation
With increasing globalization and immigration, various studies have estimated that about half of the world population is bilingual. Consequently, individuals concurrently use two or more languages or dialects in casual c…
Semantic SimilaritySemantic Textual SimilaritySentiment AnalysisSentiment ClassificationBeyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations
Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners. However, most existing zero-shot methods involve heavy human engineering or complicated self-training pipelines, hindering thei…
ClusteringSentenceSentence EmbeddingsSentiment Analysis+5NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation
While counterfactual data augmentation offers a promising step towards robust generalization in natural language processing, producing a set of counterfactuals that offer valuable inductive bias for models remains a chal…
counterfactualData AugmentationDiversityInductive Bias+4Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals
For text classification tasks, finetuned language models perform remarkably well. Yet, they tend to rely on spurious patterns in training data, thus limiting their performance on out-of-distribution (OOD) test data. Amon…
ClassificationcounterfactualSentiment AnalysisSentiment Classification+2Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models
Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks. In Natural Language Processing (NLP), DNNs are often backdoored during the fine-tuning process of a large-scale Pre-trained Language Model (PLM)…
Language ModellingSentenceSentence-Pair ClassificationSentiment Analysis+2Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information. I…
Contrastive LearningLanguage ModelingLanguage ModellingSentence+2Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment Analysis
Most existing methods focus on sentiment analysis of textual data. However, recently there has been a massive use of images and videos on social platforms, motivating sentiment analysis from other modalities. Current stu…
Multimodal Sentiment AnalysisSentiment AnalysisSentiment ClassificationTransfer LearningBootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework
Text augmentation is an effective technique for addressing the problem of insufficient data in natural language processing. However, existing text augmentation methods tend to focus on few-shot scenarios and usually perf…
ClassificationData AugmentationLanguage ModellingSentence+5Explaining Patterns in Data with Language Models via Interpretable Autoprompting
Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks. In this work, we explore whether we can leverage this learned ability to find and explain patterns i…
Explanation GenerationNatural Language UnderstandingRerankingscientific discovery+2Unsupervised Data Augmentation for Aspect Based Sentiment Analysis
Recent approaches to Aspect-based Sentiment Analysis (ABSA) take a co-extraction approach to this span-level classification task, performing the subtasks of aspect term extraction (ATE) and aspect sentiment classificatio…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)ClassificationData Augmentation+5Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification
Target-oriented multimodal sentiment classification (TMSC) is a new subtask of aspect-based sentiment analysis, which aims to determine the sentiment polarity of the opinion target mentioned in a (sentence, image) pair. …
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)SentenceSentiment Analysis+1Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification
Aspect-based sentiment analysis (ABSA) has drawn more and more attention because of its extensive applications. However, towards the sentence carried with more than one aspect, most existing works generate an aspect-spec…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)SentenceSentiment Analysis+1One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification
Knowledge distillation is an effective method to transfer knowledge from a large pre-trained teacher model to a compacted student model. However, in previous studies, the distilled student models are still large and rema…
Ensemble LearningKnowledge DistillationSentiment AnalysisSentiment ClassificationLearnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis
Dependency tree-based methods might be susceptible to the dependency tree due to that they inevitably introduce noisy information and neglect the rich relation information between words. In this paper, we propose a learn…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Language ModelingLanguage Modelling+4Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification
Sentiment analysis is essential to process and understand unstructured user-generated content for better data analytics and decision-making. State-of-the-art techniques suffer from a high dimensional feature space becaus…
ClassificationDecision Makingfeature selectionSentiment Analysis+4Span Extraction Aided Improved Code-mixed Sentiment Classification
Sentiment classification is a fundamental NLP task of detecting the sentiment polarity of a given text. In this paper we show how solving sentiment span extraction as an auxiliary task can help improve final sentiment cl…
ClassificationMulti-Task LearningSentiment AnalysisSentiment ClassificationDoes Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task
[Purpose] To understand the meaning of a sentence, humans can focus on important words in the sentence, which reflects our eyes staying on each word in different gaze time or times. Thus, some studies utilize eye-trackin…
SentenceSentiment AnalysisSentiment ClassificationZero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition
As aspect-level sentiment labels are expensive and labor-intensive to acquire, zero-shot aspect-level sentiment classification is proposed to learn classifiers applicable to new domains without using any annotated aspect…
ClassificationSentiment AnalysisSentiment Classification