Papers Sentiment Classification
“Sentiment Classification” 태그가 달린 논문 949편 · 필터 해제
Sentiment Analysis Across Multiple African Languages: A Current Benchmark
Sentiment analysis is a fundamental and valuable task in NLP. However, due to limitations in data and technological availability, research into sentiment analysis of African languages has been fragmented and lacking. Wit…
Language ModelingLanguage ModellingSentiment AnalysisSentiment ClassificationFoundation Model's Embedded Representations May Detect Distribution Shift
Sampling biases can cause distribution shifts between train and test datasets for supervised learning tasks, obscuring our ability to understand the generalization capacity of a model. This is especially important consid…
Sentiment AnalysisSentiment ClassificationTransfer LearningFinEntity: Entity-level Sentiment Classification for Financial Texts
In the financial domain, conducting entity-level sentiment analysis is crucial for accurately assessing the sentiment directed toward a specific financial entity. To our knowledge, no publicly available dataset currently…
ClassificationSentiment AnalysisSentiment ClassificationUtilizing Weak Supervision To Generate Indonesian Conservation Dataset
Weak supervision has emerged as a promising approach for rapid and large-scale dataset creation in response to the increasing demand for accelerated NLP development. By leveraging labeling functions, weak supervision all…
ClassificationMulti-class ClassificationSentiment AnalysisSentiment ClassificationKey-phrase boosted unsupervised summary generation for FinTech organization
With the recent advances in social media, the use of NLP techniques in social media data analysis has become an emerging research direction. Business organizations can particularly benefit from such an analysis of social…
Intent DetectionSentiment AnalysisSentiment ClassificationText SummarizationRethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification
Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars. However, current multimodal models have reached a performance bottleneck. To investigate the causes of…
Sentiment AnalysisSentiment ClassificationArabic Sentiment Analysis with Noisy Deep Explainable Model
Sentiment Analysis (SA) is an indispensable task for many real-world applications. Compared to limited resourced languages (i.e., Arabic, Bengali), most of the research on SA are conducted for high resourced languages (i…
Arabic Sentiment AnalysisSentiment AnalysisSentiment ClassificationBELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision
This paper presents BELT, a novel model and learning framework for the pivotal topic of brain-to-language translation research. The translation from noninvasive brain signals into readable natural language has the potent…
Brain DecodingContrastive LearningEEGQuantization+6Stock Market Sentiment Classification and Backtesting via Fine-tuned BERT
With the rapid development of big data and computing devices, low-latency automatic trading platforms based on real-time information acquisition have become the main components of the stock trading market, so the topic o…
regressionSentiment AnalysisSentiment ClassificationAttentionMix: Data augmentation method that relies on BERT attention mechanism
The Mixup method has proven to be a powerful data augmentation technique in Computer Vision, with many successors that perform image mixing in a guided manner. One of the interesting research directions is transferring t…
Data AugmentationSentiment AnalysisSentiment ClassificationIn-Context Learning for Text Classification with Many Labels
In-context learning (ICL) using large language models for tasks with many labels is challenging due to the limited context window, which makes it difficult to fit a sufficient number of examples in the prompt. In this pa…
ClassificationIn-Context Learningintent-classificationIntent Classification+5Has Sentiment Returned to the Pre-pandemic Level? A Sentiment Analysis Using U.S. College Subreddit Data from 2019 to 2022
As impact of COVID-19 pandemic winds down, both individuals and society gradually return to pre-pandemic activities. This study aims to explore how people's emotions have changed from the pre-pandemic during the pandemic…
Graph AttentionSentiment AnalysisSentiment ClassificationUSA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech Dataset
Sentiment analysis is a pivotal task in the domain of natural language processing. It encompasses both text-level sentiment polarity classification and word-level Part of Speech(POS) sentiment polarity determination. Suc…
ClassificationPOSSentiment AnalysisSentiment Classification+2Perceptual and Task-Oriented Assessment of a Semantic Metric for ASR Evaluation
Automatic speech recognition (ASR) systems have become a vital part of our everyday lives through their many applications. However, as much as we have developed in this regard, our most common evaluation method for ASR s…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)named-entity-recognitionNamed Entity Recognition+4A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training
The successful application of large pre-trained models such as BERT in natural language processing has attracted more attention from researchers. Since the BERT typically acts as an end-to-end black box, classification s…
ClassificationSentiment AnalysisSentiment ClassificationAspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification
Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous works have made remarkabl…
ManagementSentiment AnalysisSentiment ClassificationTransforming Sentiment Analysis in the Financial Domain with ChatGPT
Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment ana…
Prompt EngineeringSentiment AnalysisSentiment ClassificationA Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition
The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously. However, current methods encode the dialo…
Contrastive Learningfeature selectionSentiment AnalysisSentiment ClassificationAnalysis of the Evolution of Advanced Transformer-Based Language Models: Experiments on Opinion Mining
Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the…
Emotion RecognitionOpinion MiningSentiment AnalysisSentiment Classification+1DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation
Self-training emerges as an important research line on domain adaptation. By taking the model's prediction as the pseudo labels of the unlabeled data, self-training bootstraps the model with pseudo instances in the targe…
Domain AdaptationMeta-LearningSentiment AnalysisSentiment Classification