Papers Sentiment Classification
“Sentiment Classification” 태그가 달린 논문 949편 · 필터 해제
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification
Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non-trivial for deep neural networks as con…
Lifelong learningNetwork PruningSentiment AnalysisSentiment ClassificationExplicit Interaction Network for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) aims to recognize targets, their sentiment polarities and opinions explaining the sentiment from a sentence. ASTE could be naturally divided into 3 atom subtasks, namely target …
Aspect Sentiment Triplet ExtractionSentenceSentiment AnalysisSentiment Classification+1SSMix: Saliency-Based Span Mixup for Text Classification
Data augmentation with mixup has shown to be effective on various computer vision tasks. Despite its great success, there has been a hurdle to apply mixup to NLP tasks since text consists of discrete tokens with variable…
ClassificationData AugmentationNatural Language InferenceSentence+4Twitter Sentiment Analysis
In this report, address the problem of sentiment classification on the Twitter dataset. used a number of machine learning and deep learning methods to perform sentiment analysis. In the end, used a majority vote ensemb…
BIG-bench Machine LearningClassificationDeep LearningEmotion Classification+5Reordering Examples Helps during Priming-based Few-Shot Learning
The ability to learn from limited data, or few-shot learning, is a desirable and often critical requirement for NLP systems. While many existing methods do poorly at learning from a handful of examples, large pretrained …
Few-Shot LearningNatural Language InferenceRetrievalSentiment Analysis+1T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERT
Sentiment analysis (SA) has become an extensive research area in recent years impacting diverse fields including ecommerce, consumer business, and politics, driven by increasing adoption and usage of social media platfor…
CPUmodelSentiment AnalysisSentiment ClassificationValidating GAN-BioBERT: A Methodology For Assessing Reporting Trends In Clinical Trials
In the past decade, there has been much discussion about the issue of biased reporting in clinical research. Despite this attention, there have been limited tools developed for the systematic assessment of qualitative st…
ClassificationSentiment AnalysisSentiment ClassificationOn the Interplay Between Fine-tuning and Composition in Transformers
Pre-trained transformer language models have shown remarkable performance on a variety of NLP tasks. However, recent research has suggested that phrase-level representations in these models reflect heavy influences of le…
Sentiment AnalysisSentiment ClassificationSentiment analysis in tweets: an assessment study from classical to modern text representation models
With the growth of social medias, such as Twitter, plenty of user-generated data emerge daily. The short texts published on Twitter -- the tweets -- have earned significant attention as a rich source of information to gu…
Decision MakingLanguage ModellingSentiment AnalysisSentiment ClassificationExploiting Position Bias for Robust Aspect Sentiment Classification
Aspect sentiment classification (ASC) aims at determining sentiments expressed towards different aspects in a sentence. While state-of-the-art ASC models have achieved remarkable performance, they are recently shown to s…
ClassificationPositionSentenceSentiment Analysis+1PTR: Prompt Tuning with Rules for Text Classification
Fine-tuned pre-trained language models (PLMs) have achieved awesome performance on almost all NLP tasks. By using additional prompts to fine-tune PLMs, we can further stimulate the rich knowledge distributed in PLMs to b…
ClassificationNatural Language InferenceRelation ClassificationSentiment Analysis+3Towards Target-dependent Sentiment Classification in News Articles
Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as…
ArticlesClassificationDecision MakingNatural Language Understanding+2Rationalization through Concepts
Automated predictions require explanations to be interpretable by humans. One type of explanation is a rationale, i.e., a selection of input features such as relevant text snippets from which the model computes the outco…
Sentiment AnalysisSentiment ClassificationSentiment Classification in Swahili Language Using Multilingual BERT
The evolution of the Internet has increased the amount of information that is expressed by people on different platforms. This information can be product reviews, discussions on forums, or social media platforms. Accessi…
ClassificationDiversityGeneral ClassificationOpinion Mining+2Learning to Share by Masking the Non-shared for Multi-domain Sentiment Classification
Multi-domain sentiment classification deals with the scenario where labeled data exists for multiple domains but insufficient for training effective sentiment classifiers that work across domains. Thus, fully exploiting …
General ClassificationMulti-Domain Sentiment ClassificationSentenceSentiment Analysis+1COVID-19 sentiment analysis via deep learning during the rise of novel cases
Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19…
Language ModelingLanguage ModellingSentiment AnalysisSentiment ClassificationSetConv: A New Approach for Learning from Imbalanced Data
For many real-world classification problems, e.g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high. To address this problem, we…
BIG-bench Machine LearningClassificationGeneral ClassificationSentiment Analysis+1Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set
Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. In this paper, we introduce LaRoSeDa, a Large Romanian Se…
ClusteringSentiment AnalysisSentiment ClassificationText Categorization+1NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles
Previous research on target-dependent sentiment classification (TSC) has mostly focused on reviews, social media, and other domains where authors tend to express sentiment explicitly. In this paper, we investigate TSC in…
ArticlesDecision MakingLanguage ModelingLanguage Modelling+2The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings
The growing availability of powerful mobile devices and other edge devices, together with increasing regulatory and security concerns about the exchange of personal information across networks of these devices has challe…
Sentiment AnalysisSentiment ClassificationWord Embeddings