Conceitos Básicos
Using a question-answering framework with BART, this study aims to extract emotion-laden phrases from tweets to enhance sentiment analysis.
Resumo
Abstract:
Sentiment analysis focuses on emotional aspects in text.
Existing methods often overlook specific sentiment phrases.
Utilizing BART for phrase extraction enhances sentiment analysis comprehensively.
Introduction:
Challenges in sentiment analysis include natural language complexity and context-dependence of emotions.
Traditional approaches use linguistic rules for sentiment classification.
Methodology:
Two-step approach: formulating a question and utilizing BART for phrase extraction.
Data preprocessing involved adding special tokens for input format suitability.
Data Extraction:
"We achieved an end loss of 87% and Jaccard score of 0.61."
Results:
Evaluation metrics included total loss and Jaccard score, showing model performance.
Conclusion:
Proposed approach leverages BART for precise emotion phrase extraction in tweets.
Estatísticas
We achieved an end loss of 87% and Jaccard score of 0.61.