RT book part
T1 Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights
A1 Guerrero Contreras, Gabriel José
A1 Balderas Díaz, Sara
A1 Serrano Fernández, Alejandro
A1 Muñoz Ortega, Andrés
A2 Ingeniería Informática
K1 Sentiment Analysis
K1 Natural Language Processing
K1 Deep Learning
K1 Hybrid Models
K1 Transformer-based Models
AB Sentiment analysis has gained prominence in the digital era, particularly within social media contexts. This study explores the integration of metadata, such as reposts, favorites, or followers, alongside textual information for sentiment analysis. A dataset of posts in Spanish is created, filtering by a set of keywords (a total of 17) of interest to us. A total of 3,962 posts were collected along with their metadata. Then, they have been manually classified as positive, negative, and neutral, and processed. The research studies and compares different models like LightGBM, BERT, and a hybrid model combining BERT outputs with metadata. Evaluation metrics, such as F1-score and precision, were employed, revealing that the hybrid model achieved the highest F1-score of 69.4%, showcasing the effectiveness of merging text comprehension capabilities with contextual metadata. While the incorporation of metadata enhanced overall sentiment classification, precision varied across sentiment classes, with notable improvements in neutral and positive sentiments, over 16% and 8% respectively.
PB IEEE
SN 979-8-3503-8679-0
SN 2472-7571
YR 2024
FD 2024-07-23
LK http://hdl.handle.net/10498/34099
UL http://hdl.handle.net/10498/34099
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026