Papers Sentiment Classification
“Sentiment Classification” 태그가 달린 논문 949편 · 필터 해제
Expanding Vietnamese SentiWordNet to Improve Performance of Vietnamese Sentiment Analysis Models
Sentiment analysis is one of the most crucial tasks in Natural Language Processing (NLP), involving the training of machine learning models to classify text based on the polarity of opinions. Pre-trained Language Models …
Sentiment AnalysisSentiment ClassificationVietnamese Sentiment AnalysisDynamic Multimodal Sentiment Analysis: Leveraging Cross-Modal Attention for Enabled Classification
This paper explores the development of a multimodal sentiment analysis model that integrates text, audio, and visual data to enhance sentiment classification. The goal is to improve emotion detection by capturing the com…
Multimodal Sentiment AnalysisSentiment AnalysisSentiment ClassificationEnhancing Multi-Modal Video Sentiment Classification Through Semi-Supervised Clustering
Understanding emotions in videos is a challenging task. However, videos contain several modalities which make them a rich source of data for machine learning and deep learning tasks. In this work, we aim to improve video…
ClusteringSentiment AnalysisSentiment ClassificationELECTRA and GPT-4o: Cost-Effective Partners for Sentiment Analysis
Bidirectional transformers excel at sentiment analysis, and Large Language Models (LLM) are effective zero-shot learners. Might they perform better as a team? This paper explores collaborative approaches between ELECTRA …
Sentiment AnalysisSentiment ClassificationOn the Applicability of Zero-Shot Cross-Lingual Transfer Learning for Sentiment Classification in Distant Language Pairs
This research explores the applicability of cross-lingual transfer learning from English to Japanese and Indonesian using the XLM-R pre-trained model. The results are compared with several previous works, either by model…
Cross-Lingual TransferSentiment AnalysisSentiment ClassificationDistilling Fine-grained Sentiment Understanding from Large Language Models
Fine-grained sentiment analysis (FSA) aims to extract and summarize user opinions from vast opinionated text. Recent studies demonstrate that large language models (LLMs) possess exceptional sentiment understanding capab…
Sentiment AnalysisSentiment ClassificationZero-shot Sentiment ClassificationThree-Class Text Sentiment Analysis Based on LSTM
Sentiment analysis is a crucial task in natural language processing (NLP) with applications in public opinion monitoring, market research, and beyond. This paper introduces a three-class sentiment classification method f…
Feature EngineeringSentiment AnalysisSentiment ClassificationRobustness of Large Language Models Against Adversarial Attacks
The increasing deployment of Large Language Models (LLMs) in various applications necessitates a rigorous evaluation of their robustness against adversarial attacks. In this paper, we present a comprehensive study on the…
Sentiment AnalysisSentiment ClassificationSST-2SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems
Sentiment analysis is an essential component of natural language processing, used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer fee…
Sentiment AnalysisSentiment ClassificationLook Ahead Text Understanding and LLM Stitching
This paper proposes a look ahead text understanding problem with look ahead section identification (LASI) as an example. This problem may appear in generative AI as well as human interactions, where we want to understand…
Sentiment AnalysisSentiment ClassificationMultimodal Sentiment Analysis Based on Causal Reasoning
With the rapid development of multimedia, the shift from unimodal textual sentiment analysis to multimodal image-text sentiment analysis has obtained academic and industrial attention in recent years. However, multimodal…
Causal InferencecounterfactualCounterfactual InferenceMultimodal Sentiment Analysis+2BERTCaps: BERT Capsule for Persian Multi-Domain Sentiment Analysis
Multidomain sentiment analysis involves estimating the polarity of an unstructured text by exploiting domain specific information. One of the main issues common to the approaches discussed in the literature is their poor…
Binary ClassificationClassificationdomain classificationSentiment Analysis+1Uniform Discretized Integrated Gradients: An effective attribution based method for explaining large language models
Integrated Gradients is a well-known technique for explaining deep learning models. It calculates feature importance scores by employing a gradient based approach computing gradients of the model output with respect to i…
BenchmarkingFeature ImportanceQuestion AnsweringSentiment Analysis+2Enhancing Sentiment Analysis in Bengali Texts: A Hybrid Approach Using Lexicon-Based Algorithm and Pretrained Language Model Bangla-BERT
Sentiment analysis (SA) is a process of identifying the emotional tone or polarity within a given text and aims to uncover the user's complex emotions and inner feelings. While sentiment analysis has been extensively stu…
Language ModelingLanguage ModellingSentiment AnalysisSentiment ClassificationSentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment
With strong expressive capabilities in Large Language Models(LLMs), generative models effectively capture sentiment structures and deep semantics, however, challenges remain in fine-grained sentiment classification acros…
ClassificationDecision MakingEmotion ClassificationLanguage Modeling+6Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network
Sentiment analysis is pivotal in Natural Language Processing for understanding opinions and emotions in text. While advancements in Sentiment analysis for English are notable, Arabic Sentiment Analysis (ASA) lags, despit…
Arabic Sentiment AnalysisSentiment AnalysisSentiment ClassificationLayer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity
Evaluating the importance of different layers in large language models (LLMs) is crucial for optimizing model performance and interpretability. This paper first explores layer importance using the Activation Variance-Spa…
Contrastive LearningHallucinationLanguage ModelingLanguage Modelling+5The Moral Foundations Weibo Corpus
Moral sentiments expressed in natural language significantly influence both online and offline environments, shaping behavioral styles and interaction patterns, including social media selfpresentation, cyberbullying, adh…
Decision MakingSentiment AnalysisSentiment ClassificationTinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation
Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, often raise privacy concerns and demand significant on-device computational resources. Centralized learning (CL) on th…
ClassificationFederated LearningPrivacy PreservingSentiment Analysis+1A Multilingual Sentiment Lexicon for Low-Resource Language Translation using Large Languages Models and Explainable AI
South Africa and the Democratic Republic of Congo (DRC) present a complex linguistic landscape with languages such as Zulu, Sepedi, Afrikaans, French, English, and Tshiluba (Ciluba), which creates unique challenges for A…
Large Language ModelSentiment AnalysisSentiment ClassificationTranslation