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Research
Monalisha Ghosh
NIT Durgapur
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5827-8222
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Sentiment Analysis has recently been considered as the most active research field in the natural language processing (NLP) domain. Deep Learning is a subset of the large family of Machine Learning and becoming a growing trend due to its automatic learning capability with impressive consequences across different NLP tasks. Hence, a fusion-based Machine Learning framework has been attempted by merging the Traditional Machine Learning method with Deep Learning techniques to tackle the challenge of sentiment prediction for a massive amount of unstructured review dataset. The proposed architecture aims to utilize the Convolutional Neural Network (CNN) with a backpropagation algorithm to extract embedded feature vectors from the top hidden layer. Thereafter, these vectors augmented to an optimized feature set generated from binary particle swarm optimization (BPSO) method. Finally, a traditional SVM classifier is trained with these extended features set to determine the optimal hyper-plane for separating two classes of review datasets. The evaluation of this research work has been carried out on two benchmark movie review datasets IMDB, SST2. Experimental results with comparative studies based on performance accuracy and F-score value are reported to highlight the benefits of the developed frameworks.
Keywords
Deep Learning framework, Sentiment Analysis, Binary PSO method, Convolutional Neural Network (CNN), Support Vector Machine.
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CURRENT STATUS: Under Revision
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Posted 05 Aug, 2020
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Editorial decision: Minor Revision
On 13 Aug, 2020
Reviewer #2 agreed
On 03 Aug, 2020
Reviewer #3 agreed
On 03 Aug, 2020
Reviewer #4 agreed
On 03 Aug, 2020
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Received 03 Aug, 2020
Review #3 received
Received 03 Aug, 2020
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Received 02 Aug, 2020
Editor assigned
On 30 Jul, 2020
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Reviewer #1 agreed
On 30 Jul, 2020
Submission checks complete
On 29 Jul, 2020
Editor invited
On 29 Jul, 2020
No comments provided
Editorial decision: Major Revision
On 20 Feb, 2020
Review #3 received
Received 19 Feb, 2020
Review #5 received
Received 17 Feb, 2020
Reviewer #9 agreed
On 14 Feb, 2020
Review #6 received
Received 13 Feb, 2020
Review #7 received
Received 13 Feb, 2020
Reviewer #7 agreed
On 13 Feb, 2020
Reviewer #8 agreed
On 13 Feb, 2020
Review #2 received
Received 11 Feb, 2020
Review #1 received
Received 11 Feb, 2020
Reviewer #5 agreed
On 10 Feb, 2020
Reviewer #6 agreed
On 10 Feb, 2020
Reviewer #4 agreed
On 09 Feb, 2020
Review #4 received
Received 09 Feb, 2020
Reviewers invited
Invitations sent on 07 Feb, 2020
Reviewer #1 agreed
On 07 Feb, 2020
Reviewer #2 agreed
On 07 Feb, 2020
Reviewer #3 agreed
On 07 Feb, 2020
First submitted
On 21 Jan, 2020
Editor assigned
On 21 Jan, 2020
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On 20 Jan, 2020
Editor invited
On 20 Jan, 2020
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This preprint is under consideration at Journal of Big Data. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Monalisha Ghosh
NIT Durgapur
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5827-8222
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 2
Posted 05 Aug, 2020
No community comments so far
Editorial decision: Minor Revision
On 13 Aug, 2020
Reviewer #2 agreed
On 03 Aug, 2020
Reviewer #3 agreed
On 03 Aug, 2020
Reviewer #4 agreed
On 03 Aug, 2020
Review #2 received
Received 03 Aug, 2020
Review #3 received
Received 03 Aug, 2020
Review #4 received
Received 03 Aug, 2020
Review #1 received
Received 02 Aug, 2020
Editor assigned
On 30 Jul, 2020
Reviewers invited
Invitations sent on 30 Jul, 2020
Reviewer #1 agreed
On 30 Jul, 2020
Submission checks complete
On 29 Jul, 2020
Editor invited
On 29 Jul, 2020
No comments provided
Editorial decision: Major Revision
On 20 Feb, 2020
Review #3 received
Received 19 Feb, 2020
Review #5 received
Received 17 Feb, 2020
Reviewer #9 agreed
On 14 Feb, 2020
Review #6 received
Received 13 Feb, 2020
Review #7 received
Received 13 Feb, 2020
Reviewer #7 agreed
On 13 Feb, 2020
Reviewer #8 agreed
On 13 Feb, 2020
Review #2 received
Received 11 Feb, 2020
Review #1 received
Received 11 Feb, 2020
Reviewer #5 agreed
On 10 Feb, 2020
Reviewer #6 agreed
On 10 Feb, 2020
Reviewer #4 agreed
On 09 Feb, 2020
Review #4 received
Received 09 Feb, 2020
Reviewers invited
Invitations sent on 07 Feb, 2020
Reviewer #1 agreed
On 07 Feb, 2020
Reviewer #2 agreed
On 07 Feb, 2020
Reviewer #3 agreed
On 07 Feb, 2020
First submitted
On 21 Jan, 2020
Editor assigned
On 21 Jan, 2020
Submission checks complete
On 20 Jan, 2020
Editor invited
On 20 Jan, 2020
Subject Areas
Theoretical Computer Science
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Sentiment Analysis has recently been considered as the most active research field in the natural language processing (NLP) domain. Deep Learning is a subset of the large family of Machine Learning and becoming a growing trend due to its automatic learning capability with impressive consequences across different NLP tasks. Hence, a fusion-based Machine Learning framework has been attempted by merging the Traditional Machine Learning method with Deep Learning techniques to tackle the challenge of sentiment prediction for a massive amount of unstructured review dataset. The proposed architecture aims to utilize the Convolutional Neural Network (CNN) with a backpropagation algorithm to extract embedded feature vectors from the top hidden layer. Thereafter, these vectors augmented to an optimized feature set generated from binary particle swarm optimization (BPSO) method. Finally, a traditional SVM classifier is trained with these extended features set to determine the optimal hyper-plane for separating two classes of review datasets. The evaluation of this research work has been carried out on two benchmark movie review datasets IMDB, SST2. Experimental results with comparative studies based on performance accuracy and F-score value are reported to highlight the benefits of the developed frameworks.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This preprint is available for download as a PDF.
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