Department
Electrical and Computer Engineering
Document Type
Article
Publication Title
Machine Learning with Applications
ISSN
2666-8270
Volume
15
DOI
10.1016/j.mlwa.2023.100522
First Page
1
Last Page
15
Publication Date
3-2024
Abstract
We present ChatReview, a ChatGPT-enabled natural language processing framework that effectively studies domain-specific user reviews to offer relevant and personalized search results at multiple levels of granularity. The framework accomplishes this task using four phases including data collection, tokenization, query construction, and response generation. The data collection phase involves gathering domain-specific user reviews from public and private repositories. In the tokenization phase, ChatReview applies sentiment analysis to extract keywords and categorize them into various sentiment classes. This process creates a token repository that best describes the user sentiments for a given user-review data. In the query construction phase, the framework uses the token repository and domain knowledge to construct three types of ChatGPT prompts including explicit, implicit, and creative. In the response generation phase, ChatReview pipelines these prompts into ChatGPT to generate search results at varying levels of granularity.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Ho, B.,
Mayberry, T.,
Nguyen, K.,
Dhulipala, M.,
&
Krishnamani Pallipuram, V.
(2024).
ChatReview: A ChatGPT-enabled natural language processing framework to study domain-specific user reviews.
Machine Learning with Applications, 15, 1–15.
DOI: 10.1016/j.mlwa.2023.100522
https://scholarlycommons.pacific.edu/soecs-facarticles/317
Comments
View the code for this project on GitHub: https://github.com/vkpallipuram/ChatREVIEW