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It can be accepted now as the paper is improved
1. The article is clear in understanding and standard structure has been used.
2. Sufficient literature has been surveyed but lacks standard journal references like IEEE transactions, Elsevier springer etc.
3. The data set used is standard datasets,
1.Sentiment analysis using machine learning is one of the upcoming area and is within aim and scope.
2. Extensive investigation is performed with machine learning and deep learning models.
1. findings provided in the table are valid.
2. Deep learning method has been tried.
1. All suggestions mentioned in the previous review has been incorporated.
Please revise, as per the reviewers' comments
[# PeerJ Staff Note: Please ensure that all review comments are addressed in a rebuttal letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the rebuttal letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the rebuttal letter. Directions on how to prepare a rebuttal letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #]
Clear and unambiguous
Literature review is fair
Figures and Tables are clear
Novelty is lacking only application of existing algorithm with GPU based infrastructure
Good choice of algorithm and datasets
1. Paper does not reveal whether multiple modalities have been used for sentiment Analysis. It can be mentioned.
2. Sarcasm is not part analysis and can be added
1. The article is clear in understanding and standard structure has been used.
2. Sufficient literature has been surveyed but lacks standard journal references like IEEE transactions, Elsevier springer etc.
3. The data set used is standard datasets,
1.Sentiment analysis using machine learning is one of the upcoming area and is within aim and scope.
2. Extensive investigation is performed with machine learning and deep learning models.
3. I could see only fasttext comparison with amazon review and all other datasets mentioned, but LSVM and SA-BLSTM of existing literature is not shown for amazon, yelp dataset.
1. findings provided in the table are valid.
2. Conclusion should highlight the accuracy measure of the proposed model
Highlight what is the % of performance evaluation in abstract also
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