Open Access
Open access
Taylor & Francis
Journal of Engineering Design
,  pages 1-26

An optimised deep learning model with an Atrous convolutional-based inception system for the sentiment analysis of customer online reviews

V. Vaissnave 1
, 
S. Selva Birunda 2
, 
V. Dharani 3
, 
R. Lalitha 3
, 
K. Muthamil Sudar 4
1
 
2
 
Ramco Institute of Technology
3
 
4
 
Publication type: Journal Article
Publication date: 2025-03-07
Taylor & Francis
scimago Q2
wos Q1
SJR: 0.539
CiteScore: 4.6
Impact factor: 3.4
ISSN: 09544828, 14661837
Found 

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Vaissnave V. et al. An optimised deep learning model with an Atrous convolutional-based inception system for the sentiment analysis of customer online reviews // Journal of Engineering Design. 2025. pp. 1-26.
GOST all authors (up to 50) Copy
Vaissnave V., Birunda S. S., Dharani V., Lalitha R., Muthamil Sudar K. An optimised deep learning model with an Atrous convolutional-based inception system for the sentiment analysis of customer online reviews // Journal of Engineering Design. 2025. pp. 1-26.
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RIS Copy
TY - JOUR
DO - 10.1080/09544828.2025.2475426
UR - https://www.tandfonline.com/doi/full/10.1080/09544828.2025.2475426
TI - An optimised deep learning model with an Atrous convolutional-based inception system for the sentiment analysis of customer online reviews
T2 - Journal of Engineering Design
AU - Vaissnave, V.
AU - Birunda, S. Selva
AU - Dharani, V.
AU - Lalitha, R.
AU - Muthamil Sudar, K.
PY - 2025
DA - 2025/03/07
PB - Taylor & Francis
SP - 1-26
SN - 0954-4828
SN - 1466-1837
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2025_Vaissnave,
author = {V. Vaissnave and S. Selva Birunda and V. Dharani and R. Lalitha and K. Muthamil Sudar},
title = {An optimised deep learning model with an Atrous convolutional-based inception system for the sentiment analysis of customer online reviews},
journal = {Journal of Engineering Design},
year = {2025},
publisher = {Taylor & Francis},
month = {mar},
url = {https://www.tandfonline.com/doi/full/10.1080/09544828.2025.2475426},
pages = {1--26},
doi = {10.1080/09544828.2025.2475426}
}