RT info:eu-repo/semantics/article
T1 Social Network Sentiment Analysis Using Hybrid Deep Learning Models
A1 Merayo Álvarez, Noemí
A1 Vegas Hernández, Jesús María
A1 Llamas Bello, César
A1 Fernández del Reguero, Patricia
K1 deep learning
K1 hybrid strategies
K1 sentiment analysis
K1 social networks
K1 Twitter
K1 Spanish
AB The exponential growth in information on the Internet, particularly within social networks, highlights the importance of sentiment and opinion analysis. The intrinsic characteristics of the Spanish language coupled with the short length and lack of context of messages on social media pose a challenge for sentiment analysis in social networks. In this study, we present a hybrid deep learning model combining convolutional and long short-term memory layers to detect polarity levels in Twitter for the Spanish language. Our model significantly improved the accuracy of existing approaches by up to 20%, achieving accuracies of around 76% for three polarities (positive, negative, neutral) and 91% for two polarities (positive, negative).
PB MDPI
SN 2076-3417
YR 2023
FD 2023-10-23
LK https://uvadoc.uva.es/handle/10324/64511
UL https://uvadoc.uva.es/handle/10324/64511
LA eng
NO Applied Sciences, 2023, vol. 13, n. 20, 11608
NO Producción Científica
DS UVaDOC
RD 02-dic-2024