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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM39848

Review Analyzer

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM39848

Volume:

8 |

12 |

Dec 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM39848

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DVSN

Dr. V Siva Nagaraju

VA

Vishesh Agarwal

RV

R. Vedagna

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Abstract

This project focuses on leveraging generative AI to analyse user reviews from the Play Store for diverse applications. By utilizing advanced large language models (LLMs), the system processes extensive user feedback to identify key trends, sentiments, and actionable insights. The AI analyses reviews to categorize overall user sentiment (positive, negative, or neutral), highlight recurring issues, and identify popular feature requests. The generative AI’s capabilities enable it to provide nuanced suggestions to developers, such as improving app functionality, addressing common complaints, and implementing features aligned with user preferences. Additionally, the system can generate concise summaries of user reactions, offering developers a clear understanding of their app’s strengths and areas needing improvement. This data-driven approach enhances app development by prioritizing updates based on real user needs, improving user satisfaction, and fostering higher ratings on the Play Store. The integration of generative AI streamlines the review analysis process, ensuring actionable recommendations for sustained app success.

KEYWORDS

Generative AI Large Language Models (LLMs) sentiment analysis App improvement review analysis

Cite This Article

Dr. V Siva Nagaraju, Vishesh Agarwal, R. Vedagna, "Review Analyzer," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 12, Dec 2024. https://doi.org/10.55041/IJSREM39848

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Editorial & Review Transparency

Publication Date

12/16/2024

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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Review Report (Confidential)
Copyright Form (Office)

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2

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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