An Optimized Deep Neural Aspect Based Framework for Sentiment Classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-022-10081-w.pdf
Reference41 articles.
1. Gandhi, U. D., Malarvizhi Kumar, P., Chandra Babu, G., & Karthick, G. (2021). Sentiment analysis on twitter data by using convolutional neural network (CNN) and long short term memory (LSTM). Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08580-3
2. Mahalakshmi, P., & Fatima, N. S. (2021). Ensembling of text and images using deep convolutional neural networks for intelligent information retrieval. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08211-x
3. BalaAnand, M., Karthikeyan, N., & Karthik, S. (2019). Envisioning social media information for big data using big vision schemes in wireless environment. Wireless Personal Communications, 109, 777–796. https://doi.org/10.1007/s11277-019-06590-w
4. Das, D. (2018). Positive and negative link prediction algorithm based on sentiment analysis in large social networks. Wireless Personal Communications, 102, 2183–2198. https://doi.org/10.1007/s11277-018-5499-6
5. Poria, S., Hazarika, D., Majumder, N., & Mihalcea, R. (2020). Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research. IEEE Transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2020.3038167
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1. Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework;Web Intelligence;2024-05-15
2. Correction to: An Optimized Deep Neural Aspect Based Framework for Sentiment Classification;Wireless Personal Communications;2022-12-21
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