Deep Learning Approach for Aspect-Based Sentiment Classification: A Comparative Review

A Deep Learning Approach for Human Face Sentiment Classification

2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter) ◽

10.1109/snpdwinter52325.2021.00015 ◽

2021 ◽

Author(s):

Chetanpal Singh ◽

Santoso Wibowo ◽

Srimannarayana Grandhi

Keyword(s):

Deep Learning ◽

Sentiment Classification ◽

Learning Approach ◽

Human Face

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Cross-domain sentiment classification using deep learning approach

2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems ◽

10.1109/ccis.2014.7175703 ◽

2014 ◽

Author(s):

Miao Sun ◽

Qi Tan ◽

Runwei Ding ◽

Hong Liu

Keyword(s):

Deep Learning ◽

Sentiment Classification ◽

Learning Approach ◽

Cross Domain

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A Novel Deep Learning Approach of Convolutional Neural Network and Random Forest Classifier for Fine-grained Sentiment Classification

International Journal on Electrical Engineering and Informatics ◽

10.15676/ijeei.2021.13.2.13 ◽

2021 ◽

Vol 13 (2) ◽

pp. 465-476

Author(s):

Siji George C. G. ◽

◽

B Sumathi ◽

Keyword(s):

Neural Network ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Random Forest Classifier ◽

Sentiment Classification ◽

Learning Approach ◽

Fine Grained

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Document Modeling with Hierarchical Deep Learning Approach for Sentiment Classification

Proceedings of the 2nd International Conference on Digital Signal Processing - ICDSP 2018 ◽

10.1145/3193025.3193046 ◽

2018 ◽

Author(s):

Monalisa Ghosh ◽

Goutam Sanyal

Keyword(s):

Deep Learning ◽

Sentiment Classification ◽

Learning Approach ◽

Document Modeling

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A Novel Deep Learning Approach of Convolutional Neural Network and Random Forest Classifier for Fine-grained Sentiment Classification

International Journal on Electrical Engineering and Informatics ◽

10.15676/ijeei.2020.13.2.13 ◽

2021 ◽

Vol 13 (2) ◽

pp. 465-476

Author(s):

Siji George C G ◽

◽

B Sumathi ◽

Keyword(s):

Neural Network ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Random Forest Classifier ◽

Sentiment Classification ◽

Learning Approach ◽

Fine Grained

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An Improved Deep Learning Approach based on Variant Two-State Gated Recurrent Unit and Word Embeddings for Sentiment Classification

International Journal of Advanced Computer Science and Applications ◽

10.14569/ijacsa.2020.0110174 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Muhammad Zulqarnain ◽

Suhaimi Abd ◽

Rozaida Ghazali ◽

Nazri Mohd ◽

Muhammad Aamir ◽

...

Keyword(s):

Deep Learning ◽

Sentiment Classification ◽

Learning Approach ◽

Word Embeddings ◽

Gated Recurrent Unit

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Comparison of various Activation Functions A Deep Learning Approach

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.122126 ◽

2018 ◽

Vol 6 (3) ◽

pp. 122-126

Author(s):

Mohammed Ibrahim Khan ◽

◽

Akansha Singh ◽

Anand Handa ◽

◽

...

Keyword(s):

Deep Learning ◽

Learning Approach ◽

Activation Functions

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A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/3/3 ◽

2020 ◽

Vol 17 (3) ◽

pp. 299-305 ◽

Cited By ~ 1

Author(s):

Riaz Ahmad ◽

Saeeda Naz ◽

Muhammad Afzal ◽

Sheikh Rashid ◽

Marcus Liwicki ◽

...

Keyword(s):

Deep Learning ◽

Character Recognition ◽

Data Augmentation ◽

Short Term Memory ◽

Recognition System ◽

Learning Approach ◽

Arabic Text ◽

Data Set ◽

Processing Step ◽

Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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