Attention-aware with stacked embedding for sentiment analysis of student feedback through deep learning techniques

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PeerJ Computer Science

Main article text

 

Introduction

Literature Review

Materials & Methods

Pre-processing

Lemmatizer

Tokenizer

Text embedding techniques

Fasttext

RoBERTa

Embeddings from Language Models

Stacking: ensemble encoded features

Multi-head attention mechanism

Deep learning classification models

Bi-LSTM

GRU

Bi-GRU

Experimental setup

Student feedback data set

Software and hardware

Hyper parameters

Model evaluation metrics

Precision

Recall

F1-score

Results

Comparative analysis of the proposed model with existing techniques

Limitations and domain applicability of the proposed approach

Conclusions

Supplemental Information

Additional Information and Declarations

Competing Interests

Joanna Rosak-Szyrocka is an Academic Editor for PeerJ.

Author Contributions

Shanza Zafar Malik conceived and designed the experiments, performed the experiments, performed the computation work, prepared figures and/or tables, and approved the final draft.

Khalid Iqbal performed the experiments, performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Muhammad Sharif performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, and approved the final draft.

Yaser Ali Shah conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.

Amaad Khalil analyzed the data, authored or reviewed drafts of the article, and approved the final draft.

M. Abeer Irfan analyzed the data, authored or reviewed drafts of the article, and approved the final draft.

Joanna Rosak-Szyrocka performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The data is available at figshare: Irfan, Muhammad Abeer (2024). Raw Data.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.25427797.v1.

Funding

The authors received no funding for this work.

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