Enhancing sentiment and intent analysis in public health via fine-tuned Large Language Models on tobacco and e-cigarette-related tweets

Enhancing sentiment and intent analysis in public health via fine-tuned Large Language Models on tobacco and e-cigarette-related tweets

BackgroundAccurate sentiment analysis and intent categorization of tobacco and e-cigarette-related social media content are critical for public health research, yet they necessitate specialized natural language processing approaches.ObjectiveTo compare pre-trained and fine-tuned Flan-T5 models for i...

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Bibliographic Details
Main Authors: Sherif Elmitwalli, John Mehegan, Allen Gallagher, Raouf Alebshehy
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-11-01
Series:Frontiers in Big Data
Subjects:
social media analysis
sentiment analysis (SA)
intent classification
Large Language Models (LLMs)
public health
domain adaptation
Online Access:https://www.frontiersin.org/articles/10.3389/fdata.2024.1501154/full
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https://www.frontiersin.org/articles/10.3389/fdata.2024.1501154/full

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