RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network

Article Computer Science, Artificial Intelligence

A Hybrid CNN-LSTM: A Deep Learning Approach for Consumer Sentiment Analysis Using Qualitative User-Generated Contents

Praphula Kumar Jain et al.

Summary: This study proposed a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for sentiment analysis, utilizing deep learning techniques and conducting experimental analysis on Airlinequality and Twitter airline sentiment datasets. The Keras word embedding approach was employed, resulting in an accuracy of 91.3% in sentiment analysis.

ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING (2021)

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Proceedings Paper Computer Science, Information Systems

A Comparative Evaluation of Traditional Machine Learning and Deep Learning Classification Techniques for Sentiment Analysis

Kaushik Dhola et al.

Summary: With the advancement of digital transformation technology, people use the internet and social media to share opinions and experiences, making sentiment analysis crucial for organizations to understand customer needs. This paper outlines the data processing process and performance analysis of classification algorithms for sentiment analysis.

2021 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2021) (2021)

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Article Computer Science, Information Systems

A ConvBiLSTM Deep Learning Model-Based Approach for Twitter Sentiment Classification

Sakirin Tam et al.

Summary: The article introduces the research status and methods of Twitter sentiment analysis, pointing out that using CNN or Bi-LSTM alone for sentiment analysis cannot achieve optimal results. It proposes a combination of CNN and Bi-LSTM ConvBiLSTM model, which has been successfully applied to Twitter datasets.

IEEE ACCESS (2021)

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Proceedings Paper Computer Science, Theory & Methods