Papers Sentiment Classification
“Sentiment Classification” 태그가 달린 논문 949편 · 필터 해제
Bug Destiny Prediction in Large Open-Source Software Repositories through Sentiment Analysis and BERT Topic Modeling
This study explores a novel approach to predicting key bug-related outcomes, including the time to resolution, time to fix, and ultimate status of a bug, using data from the Bugzilla Eclipse Project. Specifically, we lev…
Binary ClassificationSentiment AnalysisSentiment ClassificationVisualizing Public Opinion on X: A Real-Time Sentiment Dashboard Using VADER and DistilBERT
In the age of social media, understanding public sentiment toward major corporations is crucial for investors, policymakers, and researchers. This paper presents a comprehensive sentiment analysis system tailored for cor…
Sentiment AnalysisSentiment ClassificationText NormalizationTrusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification
One fundamental question for the social sciences today is: how much can we trust highly complex predictive models like ChatGPT? This study tests the hypothesis that subtle changes in the structure of prompts do not produ…
Large Language ModelSentiment AnalysisSentiment ClassificationSALAD: Improving Robustness and Generalization through Contrastive Learning with Structure-Aware and LLM-Driven Augmented Data
In various natural language processing (NLP) tasks, fine-tuning Pre-trained Language Models (PLMs) often leads to the issue of spurious correlations, which negatively impacts performance, particularly when dealing with o…
Contrastive LearningcounterfactualNatural Language InferenceSentence+2Breaking BERT: Gradient Attack on Twitter Sentiment Analysis for Targeted Misclassification
Social media platforms like Twitter have increasingly relied on Natural Language Processing NLP techniques to analyze and understand the sentiments expressed in the user generated content. One such state of the art NLP m…
Adversarial TextSentiment AnalysisSentiment ClassificationTwitter Sentiment AnalysisAdvancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions
The sentiment analysis task in Tamil-English code-mixed texts has been explored using advanced transformer-based models. Challenges from grammatical inconsistencies, orthographic variations, and phonetic ambiguities have…
Data AugmentationSentiment AnalysisSentiment ClassificationSentiment Classification of Thai Central Bank Press Releases Using Supervised Learning
Central bank communication plays a critical role in shaping economic expectations and monetary policy effectiveness. This study applies supervised machine learning techniques to classify the sentiment of press releases f…
Sentiment AnalysisSentiment ClassificationN2C2: Nearest Neighbor Enhanced Confidence Calibration for Cross-Lingual In-Context Learning
Recent advancements of in-context learning (ICL) show language models can significantly improve their performance when demonstrations are provided. However, little attention has been paid to model calibration and predict…
Cross-Lingual Sentiment ClassificationIn-Context LearningPredictionSentiment Analysis+1Application of Multiple Chain-of-Thought in Contrastive Reasoning for Implicit Sentiment Analysis
Implicit sentiment analysis aims to uncover emotions that are subtly expressed, often obscured by ambiguity and figurative language. To accomplish this task, large language models and multi-step reasoning are needed to i…
Sentiment AnalysisSentiment ClassificationA Multimodal Framework for Topic Propagation Classification in Social Networks
The rapid proliferation of the Internet and the widespread adoption of social networks have significantly accelerated information dissemination. However, this transformation has introduced complexities in information cap…
Sentiment AnalysisSentiment ClassificationLarge language models in finance : what is financial sentiment?
Financial sentiment has become a crucial yet complex concept in finance, increasingly used in market forecasting and investment strategies. Despite its growing importance, there remains a need to define and understand wh…
Algorithmic TradingDecision MakingSentiment AnalysisSentiment Classification+2MAGE: Multi-Head Attention Guided Embeddings for Low Resource Sentiment Classification
Due to the lack of quality data for low-resource Bantu languages, significant challenges are presented in text classification and other practical implementations. In this paper, we introduce an advanced model combining L…
ClassificationData AugmentationSentiment AnalysisSentiment Classification+2Text Classification in the LLM Era - Where do we stand?
Large Language Models revolutionized NLP and showed dramatic performance improvements across several tasks. In this paper, we investigated the role of such language models in text classification and how they compare with…
ClassificationSentiment AnalysisSentiment Classificationtext-classification+3CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment
Large language models (LLMs) struggle with compositional generalisation, limiting their ability to systematically combine learned components to interpret novel inputs. While architectural modifications, fine-tuning, and …
Data AugmentationSentiment AnalysisSentiment ClassificationLLaVAC: Fine-tuning LLaVA as a Multimodal Sentiment Classifier
We present LLaVAC, a method for constructing a classifier for multimodal sentiment analysis. This method leverages fine-tuning of the Large Language and Vision Assistant (LLaVA) to predict sentiment labels across both im…
Multimodal Sentiment AnalysisSentiment AnalysisSentiment ClassificationExplainable AI for Sentiment Analysis of Human Metapneumovirus (HMPV) Using XLNet
In 2024, the outbreak of Human Metapneumovirus (HMPV) in China, which later spread to the UK and other countries, raised significant public concern. While HMPV typically causes mild symptoms, its effects on vulnerable in…
Sentiment AnalysisSentiment ClassificationMixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback
Sentiment analysis of patient feedback from the public health domain can aid decision makers in evaluating the provided services. The current paper focuses on free-text comments in patient surveys about general practitio…
SentenceSentiment AnalysisSentiment ClassificationGeneral Embedding vs. Task-Specific Embedding: A Comparative Approach to Enhancing NLP Performance
This paper investigates the comparative effectiveness of general embeddings, derived from pre-trained models such as BERT and RoBERTa, versus task-specific embeddings, which are obtained from models pre-trained specifica…
Multi-Task Learningnamed-entity-recognitionNamed Entity Recognition+10Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages
This study explores transformer-based models such as BERT, mBERT, and XLM-R for multi-lingual sentiment analysis across diverse linguistic structures. Key contributions include the identification of XLM-R superior adapta…
Sentiment AnalysisSentiment ClassificationXLM-RLearning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models
Aspect-based sentiment analysis (ASBA) is a refined approach to sentiment analysis that aims to extract and classify sentiments based on specific aspects or features of a product, service, or entity. Unlike traditional s…
Aspect-Based Sentiment AnalysisAspect ExtractionSentiment AnalysisSentiment Classification