DPG-LSTM: An Enhanced LSTM Framework for Sentiment Analysis in Social Media Text Based on Dependency Parsing and GCN
Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation. Deep learning models that automatically learn semantic and syntactic information have recently proved effective i...
Main Authors: | Zeyu Yin, Jinsong Shao, Muhammad Jawad Hussain, Yajie Hao, Yu Chen, Xuefeng Zhang, Li Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-12-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/13/1/354 |
Similar Items
-
Lexicon-Enhanced LSTM With Attention for General Sentiment Analysis
by: Xianghua Fu, et al.
Published: (2018-01-01) -
Research on sentiment classification for netizens based on the BERT-BiLSTM-TextCNN model
by: Xuchu Jiang, et al.
Published: (2022-06-01) -
Analysis of hyperparameters in Sentiment Analysis of Movie Reviews using Bi-LSTM
by: Singh Amankumar, et al.
Published: (2022-01-01) -
BiLSTM Model With Attention Mechanism for Sentiment Classification on Chinese Mixed Text Comments
by: Li Xiaoyan, et al.
Published: (2023-01-01) -
LSTM-based sentiment analysis for stock price forecast
by: Ching-Ru Ko, et al.
Published: (2021-03-01)