Papers Sentiment Classification
“Sentiment Classification” 태그가 달린 논문 949편 · 필터 해제
Large, Small or Both: A Novel Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization
As more than 70$\%$ of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are reluctant to generate negative summaries given the input of negative texts. To address su…
Data AugmentationLanguage ModellingLarge Language ModelOpinion Summarization+2Persian Slang Text Conversion to Formal and Deep Learning of Persian Short Texts on Social Media for Sentiment Classification
The lack of a suitable tool for the analysis of conversational texts in the Persian language has made various analyses of these texts, including Sentiment Analysis, difficult. In this research, we tried to make the under…
Emotion ClassificationSentiment AnalysisSentiment ClassificationDistilled ChatGPT Topic & Sentiment Modeling with Applications in Finance
In this study, ChatGPT is utilized to create streamlined models that generate easily interpretable features. These features are then used to evaluate financial outcomes from earnings calls. We detail a training approach …
Knowledge DistillationSentiment AnalysisSentiment ClassificationTransfer LearningESG Sentiment Analysis: comparing human and language model performance including GPT
In this paper we explore the challenges of measuring sentiment in relation to Environmental, Social and Governance (ESG) social media. ESG has grown in importance in recent years with a surge in interest from the financi…
Language ModelingLanguage ModellingSentiment AnalysisSentiment ClassificationCARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean
This paper explores the challenges posed by aspect-based sentiment classification (ABSC) within pretrained language models (PLMs), with a particular focus on contextualization and hallucination issues. In order to tackle…
ClassificationHallucinationSentiment AnalysisSentiment ClassificationFrom Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter
In the rapidly evolving landscape of social media, the introduction of new emojis in Unicode release versions presents a structured opportunity to explore digital language evolution. Analyzing a large dataset of sampled …
Sentiment AnalysisSentiment ClassificationExploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA) is a fine-grained linguistics problem that entails the extraction of multifaceted aspects, opinions, and sentiments from the given text. Both standalone and compound ABSA tasks have…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Sentiment AnalysisSentiment Classification+1Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data
The ability to generate sentiment-controlled feedback in response to multimodal inputs comprising text and images addresses a critical gap in human-computer interaction. This capability allows systems to provide empathet…
DecoderMarketingQuestion AnsweringSentiment Analysis+3Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble
Recent work has shown the defense of 01 loss sign activation neural networks against image classification adversarial attacks. A public challenge to attack the models on CIFAR10 dataset remains undefeated. We ask the fol…
Adversarial AttackClassificationimage-classificationImage Classification+5In-Context Learning Can Re-learn Forbidden Tasks
Despite significant investment into safety training, large language models (LLMs) deployed in the real world still suffer from numerous vulnerabilities. One perspective on LLM safety training is that it algorithmically f…
ArticlesIn-Context LearningMisinformationSentiment Analysis+1Linguistic features for sentence difficulty prediction in ABSA
One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that …
Aspect-Based Sentiment AnalysisDiversityNatural Language UnderstandingPrediction+3A Survey of Large Language Models in Finance (FinLLMs)
Large Language Models (LLMs) have shown remarkable capabilities across a wide variety of Natural Language Processing (NLP) tasks and have attracted attention from multiple domains, including financial services. Despite t…
Named Entity Recognition (NER)Question AnsweringSentiment ClassificationSurvey+3An Information-Theoretic Approach to Analyze NLP Classification Tasks
Understanding the importance of the inputs on the output is useful across many tasks. This work provides an information-theoretic framework to analyse the influence of inputs for text classification tasks. Natural langua…
Multiple-choiceReading ComprehensionSentiment AnalysisSentiment Classification+2Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
Fine-tuning large pre-trained language models (LLMs) on particular datasets is a commonly employed strategy in Natural Language Processing (NLP) classification tasks. However, this approach usually results in a loss of m…
Sentiment AnalysisSentiment ClassificationText ClassificationWord Embeddings+1Longitudinal Sentiment Classification of Reddit Posts
We report results of a longitudinal sentiment classification of Reddit posts written by students of four major Canadian universities. We work with the texts of the posts, concentrating on the years 2020-2023. By finely t…
ClassificationSentiment AnalysisSentiment ClassificationEstimating the severity of dental and oral problems via sentiment classification over clinical reports
Analyzing authors' sentiments in texts as a technique for identifying text polarity can be practical and useful in various fields, including medicine and dentistry. Currently, due to factors such as patients' limited kno…
Decision MakingSentiment AnalysisSentiment ClassificationAre self-explanations from Large Language Models faithful?
Instruction-tuned Large Language Models (LLMs) excel at many tasks and will even explain their reasoning, so-called self-explanations. However, convincing and wrong self-explanations can lead to unsupported confidence in…
counterfactualFaithfulness CriticNatural Language InferenceQuestion Answering+2Aspect category learning and sentimental analysis using weakly supervised learning
The surge of e-commerce reviews has presented a challenge in manually annotating the vast volume of reviews to comprehend their underlying aspects and sentiments. This research focused on leveraging weakly supervised lea…
Multi-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentiment Analysis+2Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue
Large Language Models (LLMs) have demonstrated superior abilities in tasks such as chatting, reasoning, and question-answering. However, standard LLMs may ignore crucial paralinguistic information, such as sentiment, emo…
AttributeLanguage ModelingLanguage ModellingQuestion Answering+3Exploiting Contextual Target Attributes for Target Sentiment Classification
Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word …
AttributeClassificationCloze TestLanguage Modeling+3