Papers Sentiment Classification
“Sentiment Classification” 태그가 달린 논문 949편 · 필터 해제
Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines
In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment. By leveraging Natural Language Processing (NLP) and Large Language Models (LLM), we analyze se…
ManagementSentiment AnalysisSentiment ClassificationAdversarial Evasion Attack Efficiency against Large Language Models
Large Language Models (LLMs) are valuable for text classification, but their vulnerabilities must not be disregarded. They lack robustness against adversarial examples, so it is pertinent to understand the impacts of dif…
Adversarial DefenseClassificationSentiment AnalysisSentiment Classification+2COVID-19 Twitter Sentiment Classification Using Hybrid Deep Learning Model Based on Grid Search Methodology
In the contemporary era, social media platforms amass an extensive volume of social data contributed by their users. In order to promptly grasp the opinions and emotional inclinations of individuals regarding a product o…
Sentiment AnalysisSentiment ClassificationLarge Language Models as Evaluators for Recommendation Explanations
The explainability of recommender systems has attracted significant attention in academia and industry. Many efforts have been made for explainable recommendations, yet evaluating the quality of the explanations remains …
Common Sense ReasoningInstruction FollowingRecommendation SystemsSentiment Analysis+1Why is "Problems" Predictive of Positive Sentiment? A Case Study of Explaining Unintuitive Features in Sentiment Classification
Explainable AI (XAI) algorithms aim to help users understand how a machine learning model makes predictions. To this end, many approaches explain which input features are most predictive of a target label. However, such …
Sentiment AnalysisSentiment ClassificationPerformance evaluation of Reddit Comments using Machine Learning and Natural Language Processing methods in Sentiment Analysis
Sentiment analysis, an increasingly vital field in both academia and industry, plays a pivotal role in machine learning applications, particularly on social media platforms like Reddit. However, the efficacy of sentiment…
Computational EfficiencySentiment AnalysisSentiment ClassificationPTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment
Multimodal aspect-based sentiment analysis (MABSA) aims to understand opinions in a granular manner, advancing human-computer interaction and other fields. Traditionally, MABSA methods use a joint prediction approach to …
Aspect-Based Sentiment AnalysisMultimodal Sentiment AnalysisSentiment AnalysisSentiment Classification+1A Systematic Analysis on the Temporal Generalization of Language Models in Social Media
In machine learning, temporal shifts occur when there are differences between training and test splits in terms of time. For streaming data such as news or social media, models are commonly trained on a fixed corpus from…
Hate Speech Detectionnamed-entity-recognitionNamed Entity RecognitionSentiment Analysis+1Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks
The chess domain is well-suited for creating an artificial intelligence (AI) system that mimics real-world challenges, including decision-making. Throughout the years, minimal attention has been paid to investigating ins…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Decision MakingSentiment Analysis+1Analyzing Language Bias Between French and English in Conventional Multilingual Sentiment Analysis Models
Inspired by the 'Bias Considerations in Bilingual Natural Language Processing' report by Statistics Canada, this study delves into potential biases in multilingual sentiment analysis between English and French. Given a 5…
Multilingual NLPSentiment AnalysisSentiment ClassificationUniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation
Although pre-trained language models have exhibited great flexibility and versatility with prompt-based few-shot learning, they suffer from the extensive parameter size and limited applicability for inference. Recent stu…
Dataset GenerationDomain GeneralizationFew-Shot LearningSentiment Analysis+1Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States
Opinion mining is an important task in natural language processing. The MPQA Opinion Corpus is a fine-grained and comprehensive dataset of private states (i.e., the condition of a source who has an attitude which may be …
Fine-Grained Opinion AnalysisOpinion MiningSentiment AnalysisSentiment ClassificationAspect and Opinion Term Extraction Using Graph Attention Network
In this work we investigate the capability of Graph Attention Network for extracting aspect and opinion terms. Aspect and opinion term extraction is posed as a token-level classification task akin to named entity recogni…
Graph Attentionnamed-entity-recognitionNamed Entity RecognitionSentiment Analysis+2Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales
Saliency post-hoc explainability methods are important tools for understanding increasingly complex NLP models. While these methods can reflect the model's reasoning, they may not align with human intuition, making the e…
Contrastive LearningHate Speech DetectionSentiment Classificationtext-classification+1KazSAnDRA: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes
This paper presents KazSAnDRA, a dataset developed for Kazakh sentiment analysis that is the first and largest publicly available dataset of its kind. KazSAnDRA comprises an extensive collection of 180,064 reviews obtain…
ClassificationSentiment AnalysisSentiment ClassificationA Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning
Aspect-Based Sentiment Analysis (ABSA) aims to identify terms or multiword expressions (MWEs) on which sentiments are expressed and the sentiment polarities associated with them. The development of supervised models has …
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Language ModelingLanguage Modelling+5You Only Read Once: Constituency-Oriented Relational Graph Convolutional Network for Multi-Aspect Multi-Sentiment Classification
Most of the existing aspect-based sentiment analysis (ABSA) models only predict the sentiment polarity of a single aspect at a time, focusing primarily on enhancing the representation of this single aspect based on the o…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)SentenceSentiment Analysis+1Community Needs and Assets: A Computational Analysis of Community Conversations
A community needs assessment is a tool used by non-profits and government agencies to quantify the strengths and issues of a community, allowing them to allocate their resources better. Such approaches are transitioning …
Language ModellingLarge Language ModelSentiment AnalysisSentiment ClassificationFinLlama: Financial Sentiment Classification for Algorithmic Trading Applications
There are multiple sources of financial news online which influence market movements and trader's decisions. This highlights the need for accurate sentiment analysis, in addition to having appropriate algorithmic trading…
Algorithmic TradingArticlesClassificationparameter-efficient fine-tuning+2Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts
This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare, with a particular focus on the incorporation of Natural Langua…
Emotional IntelligenceSentiment AnalysisSentiment Classification