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Dear Authors,
Thank you for addressing the reviewers' comments. Your manuscript is ready for publication.
Best wishes,
[# PeerJ Staff Note - this decision was reviewed and approved by Carlos Fernandez-Lozano, a 'PeerJ Computer Science' Section Editor covering this Section #]
The authors have enhanced the manuscript greatly by exploring new trends in sentiment analysis including attention and hybrid models. The analysis of literature from the last 3-5 years enhances the background and contextualises the study adequately. Also, the technical terms used have been made more understandable; the complicated sentences elaborated in simple ways in order to make them comprehensible while still retaining the depth of the information given.
Major data preprocessing techniques like text cleaning, tokenization, vectorization are now well understood with reference to tools/ software used in this process. Experimental case scenarios have also been explained in detail, including the environment in which the application is deployed; how it processes data, and interaction with real users. New material includes the explanation of why specific hyperparameters were chosen, what model architectures were employed, and these choices have improved the study’s reliability and replication. A simplified explanation regarding the attention mechanism is provided, and its application in analyzing the text of social networks is given, which enhances its readability and applicability.
To augment the comparison of their model with other approaches the authors have gone further into explaining why the proposed model is better than CNN and RNN especially when it comes to feature handling, sequence memory and processing efficiency. The conclusion is thus informed by specific objectives for the future research that are measurable which include enhanced real-time processing as well as extended language support. Moreover, a comprehensive error analysis has been provided that also aims to discuss difficulties such as misclassification of the sentiment, including ambiguities and sarcasm.
The authors have managed to respond to all the major issues raised in the first review hence coming up with a strong manuscript. The changes made for the final version improve the readability and rigour of the work while increasing the practical use of this research.
Dear Authors,
Thank you for submitting your article. Feedback from the reviewers is now available. Your article has not been recommended for publication in its current form. However, we do encourage you to address the concerns and criticisms of the reviewers and resubmit your article once you have updated it accordingly. Before submitting the revised paper, following should also be addressed:
1. Many of the equations are part of the related sentences. Attention is needed for correct sentence formation.
2. All of the values for the parameters of all algorithms should be given.
3. Advantages, disadvantages, and limitations of the proposed method should be provided.
Best wishes,
This study innovatively proposes a multi-source data integrated dynamic pricing model, employing a hybrid architecture combining Random Forest and LSTM networks, and incorporates reinforcement learning to optimize pricing strategies in real-time. The experiments, validated on the Amazon Reviews dataset, indicate that the model significantly outperforms traditional models in consumer behavior prediction and pricing strategy stability, demonstrating its potential application in complex market environments.
Overall Weaknesses:
1. Expand the introduction to provide readers with a more comprehensive background on the challenges of dynamic pricing and explain the role of big data and AI in addressing these challenges.
2. The methodology section should provide a more detailed explanation of the multi-source data processing steps, particularly for feature extraction from unstructured data, to facilitate a better understanding of the model construction process.
3. In the experimental setup section, include a discussion on hyperparameter selection and its impact on model performance to enhance the scientific rigor of the study.
4. Strengthen the conclusion by discussing the broader implications of the research findings and suggesting potential directions for future research.
This study innovatively proposes a multi-source data integrated dynamic pricing model, employing a hybrid architecture combining Random Forest and LSTM networks, and incorporates reinforcement learning to optimize pricing strategies in real-time. The experiments, validated on the Amazon Reviews dataset, indicate that the model significantly outperforms traditional models in consumer behavior prediction and pricing strategy stability, demonstrating its potential application in complex market environments.
Overall Weaknesses:
1. Expand the introduction to provide readers with a more comprehensive background on the challenges of dynamic pricing and explain the role of big data and AI in addressing these challenges.
2. The methodology section should provide a more detailed explanation of the multi-source data processing steps, particularly for feature extraction from unstructured data, to facilitate a better understanding of the model construction process.
3. In the experimental setup section, include a discussion on hyperparameter selection and its impact on model performance to enhance the scientific rigor of the study.
4. Strengthen the conclusion by discussing the broader implications of the research findings and suggesting potential directions for future research.
This study innovatively proposes a multi-source data integrated dynamic pricing model, employing a hybrid architecture combining Random Forest and LSTM networks, and incorporates reinforcement learning to optimize pricing strategies in real-time. The experiments, validated on the Amazon Reviews dataset, indicate that the model significantly outperforms traditional models in consumer behavior prediction and pricing strategy stability, demonstrating its potential application in complex market environments.
Overall Weaknesses:
1. Expand the introduction to provide readers with a more comprehensive background on the challenges of dynamic pricing and explain the role of big data and AI in addressing these challenges.
2. The methodology section should provide a more detailed explanation of the multi-source data processing steps, particularly for feature extraction from unstructured data, to facilitate a better understanding of the model construction process.
3. In the experimental setup section, include a discussion on hyperparameter selection and its impact on model performance to enhance the scientific rigor of the study.
4. Strengthen the conclusion by discussing the broader implications of the research findings and suggesting potential directions for future research.
This study innovatively proposes a multi-source data integrated dynamic pricing model, employing a hybrid architecture combining Random Forest and LSTM networks, and incorporates reinforcement learning to optimize pricing strategies in real-time. The experiments, validated on the Amazon Reviews dataset, indicate that the model significantly outperforms traditional models in consumer behavior prediction and pricing strategy stability, demonstrating its potential application in complex market environments.
Overall Weaknesses:
1. Expand the introduction to provide readers with a more comprehensive background on the challenges of dynamic pricing and explain the role of big data and AI in addressing these challenges.
2. The methodology section should provide a more detailed explanation of the multi-source data processing steps, particularly for feature extraction from unstructured data, to facilitate a better understanding of the model construction process.
3. In the experimental setup section, include a discussion on hyperparameter selection and its impact on model performance to enhance the scientific rigor of the study.
4. Strengthen the conclusion by discussing the broader implications of the research findings and suggesting potential directions for future research.
The current review focuses heavily on foundational studies but lacks engagement with recent advancements in sentiment analysis that utilize attention mechanisms or hybrid models. Incorporate a review of literature from the past 3-5 years to strengthen the background.
The manuscript uses heavy technical jargon which could be simplified for better accessibility and understanding without compromising technical accuracy. Rewrite complex sentences to be more concise and understandable to a broader audience
The manuscript skips detailed steps involved in data preprocessing before it is fed into the model. Specify the text cleaning, tokenization, and vectorization processes used, including any tools or software employed.
The section on practical application tests is vague about the environments and conditions under which the tests were conducted. Provide details on the deployment environment, real-time data handling capabilities, and user interaction scenarios.
Details on the tuning of model parameters, such as the number of layers, units in each layer, and the rationale for these choices, are missing. Discuss how these parameters were optimized based on the characteristics of the input data.
The explanation of the attention mechanism is overly technical without clear application examples to the social media text analysis. Simplify the description and provide a concrete example of how attention improves sentiment classification in a real-world social media context.
The comparison between the proposed model and existing models like CNN and RNN is superficial. Delve deeper into why the proposed model outperforms others by discussing feature handling, sequence memory, and processing speed.
Future work is mentioned in broad terms. Narrow down to specific, measurable objectives, such as improving the model’s real-time processing capabilities or expanding its language support, which can directly follow from the concluded research.
There is no thorough error analysis to understand model failures or misclassifications. Provide a breakdown of error types encountered (e.g., false positives, false negatives), especially for edge cases or ambiguous sentiments.
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