The exponential growth of user-generated content on social networks has necessitated the development of accurate methods for extractingsentimentsfrom text.SentimentAnalysisinvolves understanding people's opinions, feelings, and attitudes towards various entities, which is crucial for applications such as improving human-computer interaction, educational tools, and conversational ...
The MAFESA framework'sexplainabilitymodule is designed to provide a clear and understandable rationale for each of itssentimentanalysispredictions. This module combines aspect contributions, text highlighting, and aspect-sentimentrelationships, enhanced by the integration of Local Interpretable Model-agnostic Explanations (LIME).
Large language models (LLMs) have transformedsentimentanalysis, yet balancing accuracy, efficiency, andexplainabilityremains a critical challenge. This study presents the first comprehensive evaluation of DeepSeek-R1--an open-source reasoning model--against OpenAI's GPT-4o and GPT-4o-mini. We test the full 671B model and its distilled variants, systematically documenting few-shot learning ...
Such details provide a deeper understanding and appreciation for Explainability Dl For Sentiment Analysis Detection.
In this timely study we carry out a first of its kind overview of keysentimentanalysistechniques and explainable artificial intelligence (AI) methodologies that are currently in use. Furthermore, we provide a comprehensive review ofsentimentanalysisexplainability. Index Terms—Sentimentanalysis, Deep Learning,Explainability...
As we can see from the illustration, Explainability Dl For Sentiment Analysis Detection has many fascinating aspects to explore.
Awesome-LLM-ExplainabilityA curated list ofexplainability-related papers, articles, and resources focused on Large Language Models (LLMs). This repository aims to provide researchers, practitioners, and enthusiasts with insights into theexplainabilityimplications, challenges, and advancements surrounding these powerful models.
Sentimentanalysisacross multiple languages remains a challenging problem due to linguistic diversity, domain-specific expressions, and the limitedexplainabilityof existing models. This study aims to address these issues by proposing the Explainable MultilingualSentimentAnalyzer (EMSA), a novel framework that integrates large language models with prompt engineering. EMSA employs a two ...
