Papers Sentiment Classification
“Sentiment Classification” 태그가 달린 논문 949편 · 필터 해제
AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis
We introduce AdaptiSent, a new framework for Multimodal Aspect-Based Sentiment Analysis (MABSA) that uses adaptive cross-modal attention mechanisms to improve sentiment classification and aspect term extraction from both…
Aspect-Based Sentiment AnalysisSentiment AnalysisSentiment ClassificationTerm ExtractionQuantum Graph Transformer for NLP Sentiment Classification
Quantum machine learning is a promising direction for building more efficient and expressive models, particularly in domains where understanding complex, structured data is critical. We present the Quantum Graph Transfor…
ClassificationLanguage ModelingLanguage ModellingQuantum Machine Learning+2Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis
We investigate the effectiveness of large language models (LLMs), including reasoning-based and non-reasoning models, in performing zero-shot financial sentiment analysis. Using the Financial PhraseBank dataset annotated…
Sentiment AnalysisSentiment ClassificationMulti-Domain ABSA Conversation Dataset Generation via LLMs for Real-World Evaluation and Model Comparison
Aspect-Based Sentiment Analysis (ABSA) offers granular insights into opinions but often suffers from the scarcity of diverse, labeled datasets that reflect real-world conversational nuances. This paper presents an approa…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Dataset GenerationSentiment Analysis+2The Role of Diversity in In-Context Learning for Large Language Models
In-context learning (ICL) is a crucial capability of current large language models (LLMs), where the selection of examples plays a key role in performance. While most existing approaches focus on selecting the most simil…
DiversityIn-Context LearningMathSentiment Analysis+1Analyzing Political Bias in LLMs via Target-Oriented Sentiment Classification
Political biases encoded by LLMs might have detrimental effects on downstream applications. Existing bias analysis methods rely on small-size intermediate tasks (questionnaire answering or political content generation) a…
SentenceSentiment AnalysisSentiment ClassificationCrosGrpsABS: Cross-Attention over Syntactic and Semantic Graphs for Aspect-Based Sentiment Analysis in a Low-Resource Language
Aspect-Based Sentiment Analysis (ABSA) is a fundamental task in natural language processing, offering fine-grained insights into opinions expressed in text. While existing research has largely focused on resource-rich la…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Dependency ParsingSemantic Similarity+3Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Space
The increasing complexity of large-scale language models has amplified concerns regarding their interpretability and reusability. While traditional embedding models like Word2Vec and GloVe offer scalability, they lack tr…
Semantic SimilaritySemantic Textual SimilaritySentiment AnalysisSentiment ClassificationDeFTX: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer
Effective cross-lingual transfer remains a critical challenge in scaling the benefits of large language models from high-resource to low-resource languages. Towards this goal, prior studies have explored many approaches …
Cross-Lingual TransferNatural Language InferenceSentiment AnalysisSentiment Classification+1A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike existing work that focuses on either persona…
SentenceSentence ClassificationSentiment AnalysisSentiment Classification+1PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore
Fine-grained sentiment analysis (FGSA) aims to identify sentiment polarity toward specific aspects within a text, enabling more precise opinion mining in domains such as product reviews and social media. However, traditi…
Aspect ExtractionOpinion MiningPrompt LearningSentiment Analysis+2Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applications
This study introduces a framework for evaluating consistency in large language model (LLM) binary text classification, addressing the lack of established reliability assessment methods. Adapting psychometric principles, …
ArticlesBinary text classificationLarge Language ModelSentiment Analysis+3Comparative sentiment analysis of public perception: Monkeypox vs. COVID-19 behavioral insights
The emergence of global health crises, such as COVID-19 and Monkeypox (mpox), has underscored the importance of understanding public sentiment to inform effective public health strategies. This study conducts a comparati…
MisinformationSentiment AnalysisSentiment ClassificationDynamic Domain Information Modulation Algorithm for Multi-domain Sentiment Analysis
Multi-domain sentiment classification aims to mitigate poor performance models due to the scarcity of labeled data in a single domain, by utilizing data labeled from various domains. A series of models that jointly train…
Classificationdomain classificationHyperparameter OptimizationMulti-Domain Sentiment Classification+2Estimating Quality in Therapeutic Conversations: A Multi-Dimensional Natural Language Processing Framework
Engagement between client and therapist is a critical determinant of therapeutic success. We propose a multi-dimensional natural language processing (NLP) framework that objectively classifies engagement quality in couns…
Semantic SimilaritySemantic Textual SimilaritySentiment AnalysisSentiment ClassificationDivide (Text) and Conquer (Sentiment): Improved Sentiment Classification by Constituent Conflict Resolution
Sentiment classification, a complex task in natural language processing, becomes even more challenging when analyzing passages with multiple conflicting tones. Typically, longer passages exacerbate this issue, leading to…
Sentiment AnalysisSentiment ClassificationEnhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models
We explore the use of Chain-of-Thought (CoT) prompting with large language models (LLMs) to improve the accuracy of granular sentiment categorization in app store reviews. Traditional numeric and polarity-based ratings o…
Sentiment AnalysisSentiment ClassificationAutomated Sentiment Classification and Topic Discovery in Large-Scale Social Media Streams
We present a framework for large-scale sentiment and topic analysis of Twitter discourse. Our pipeline begins with targeted data collection using conflict-specific keywords, followed by automated sentiment labeling via m…
Sentiment AnalysisSentiment ClassificationEmotional Analysis of Fashion Trends Using Social Media and AI: Sentiment Analysis on Twitter for Fashion Trend Forecasting
This study explores the intersection of fashion trends and social media sentiment through computational analysis of Twitter data using the T4SA (Twitter for Sentiment Analysis) dataset. By applying natural language proce…
Sentiment AnalysisSentiment ClassificationArabic Metaphor Sentiment Classification Using Semantic Information
In this paper, I discuss the testing of the Arabic Metaphor Corpus (AMC) [1] using newly designed automatic tools for sentiment classification for AMC based on semantic tags. The tool incorporates semantic emotional tags…
ClassificationSentiment AnalysisSentiment Classification