TELKOMNIKA Telecommunication, Computing, Electronics and Control
Sentiment analysis by deep learning approaches
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Sentiment analysis by deep learning approaches
Sentiment analysis by deep learning approaches
Subject
Bimodal, CNN layers, MOUD, Multimodal, Word embeddings
Description
We propose a model for carrying out deep learning based multimodal
sentiment analysis. The MOUD dataset is taken for experimentation
purposes. We developed two parallel text based and audio basedmodels and further, fused these heterogeneous feature maps taken from intermediate layers to complete thearchitecture. Performance measures–Accuracy, precision, recall and F1-score–are observed to outperformthe existing models.
sentiment analysis. The MOUD dataset is taken for experimentation
purposes. We developed two parallel text based and audio basedmodels and further, fused these heterogeneous feature maps taken from intermediate layers to complete thearchitecture. Performance measures–Accuracy, precision, recall and F1-score–are observed to outperformthe existing models.
Creator
Sreevidya P. , O. V. Ramana Murthy, S. Veni
Source
DOI: 10.12928/TELKOMNIKA.v18i2.13912
Publisher
Universitas Ahmad Dahlan
Date
April 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Sreevidya P. , O. V. Ramana Murthy, S. Veni, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Sentiment analysis by deep learning approaches,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3667.
Sentiment analysis by deep learning approaches,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3667.