Islam, M.M.; HOSSAIN, G.S.; Sarma, D.; Chakma, R.; Mynoddin, M. Deep-ABSA: A Multichannel Deep Learning Framework For Aspect-Based Bangla Sentiment Analysis. Preprints2023, 2023071204. https://doi.org/10.20944/preprints202307.1204.v1
APA Style
Islam, M.M., HOSSAIN, G.S., Sarma, D., Chakma, R., & Mynoddin, M. (2023). Deep-ABSA: A Multichannel Deep Learning Framework For Aspect-Based Bangla Sentiment Analysis. Preprints. https://doi.org/10.20944/preprints202307.1204.v1
Chicago/Turabian Style
Islam, M.M., Rishita Chakma and Md Mynoddin. 2023 "Deep-ABSA: A Multichannel Deep Learning Framework For Aspect-Based Bangla Sentiment Analysis" Preprints. https://doi.org/10.20944/preprints202307.1204.v1
Abstract
Nowadays people express their opinions on social media. Also provides product reviews on eCommerce websites and responds to various news as comments. It is necessary to know the polarity and aspect of various posts and comments for business, education and security. Aspect-based sentiment analysis (ABSA) predicts text category and polarity. In this paper, we proposed a deep learning framework Deep-ABSA for aspect-based sentiment analysis from Bangla texts. Our proposed framework is a multi-channel architecture. We implemented the word embedding, Bi-LSTM with the attention mechanism for one channel. And for another channel, we adopted the character-embedded convolutional neural network. Finally, we concatenated both channels for adjoining the features of each channel. We obtained an adequate performance from our proposed framework for aspect term analysis from Bangla sentences.
Keywords
ABSA; word embedding; Bi-LSTM; attention; character embedding
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
