Master's Student @ Shenzhen UniversityI am currently pursuing my Master's degree in Artificial Intelligence at Shenzhen University. My research focuses on big data intelligence and the digital economy, with an interdisciplinary emphasis on the integration of artificial intelligence and management science. I am particularly interested in applying machine learning, computer vision, and natural language processing to explore data-driven insights across domains.
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Shenzhen UniversityM.Eng. in Artificial IntelligenceSep. 2022 - Jun. 2025
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Zhejiang Sci-Tech UniversityB.Eng. in Digital Media TechnologySep. 2018 - Jun. 2022
Honors & Awards(view all )
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First-Class Master's Academic Scholarship, Shenzhen University2024
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National Invention Patent, CNIPA, China2024
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Meritorious Winner, Mathematical Contest in Modeling (MCM), COMAP, USA2023
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Second Prize, The 13th National Undergraduate Mathematics Competition2021
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Gold Award, The 6th Zhejiang Province International College Students’ “Internet+” Innovation and Entrepreneurship Competition2020
News
Selected Publications (view all )
Do Comments Drive Sales? Exploring the Impact of Real-Time Comments on Live Streaming E-Commerce Performance
Yaozong Li, Weina Lin, Yuanyue Feng# (# corresponding author)
Accepted by the Doctoral Consortium of the 24th Wuhan International Conference on E-Business (WHICEB) 2025-05-11
[Purpose/Significance] This study aims to deepen the understanding of how real-time comments influence sales performance in the context of live streaming e-commerce. [Methodology/Process] A theoretical framework was constructed based on the Elaboration Likelihood Model (ELM). Utilizing real data from 52 livestreams on the Douyin platform, two deep learning-based text classification models were trained and employed to conduct an in-depth analysis of comments. A generalized linear model (GLM) was then employed for empirical analysis. [Findings/Conclusion] It was found that comment length has a significantly negative effect on sales, whereas sentiments and product attention exhibit an inverted U-shaped relationship with sales, and the number of comments has a significantly positive impact on sales. The model demonstrated high explanatory power (Pseudo R² = 0.8249). [Originality/Value] Cutting-edge natural language processing (NLP) methods were applied to empirical studies in live streaming e-commerce, fostering interdisciplinary integration and advancing methodological innovation in the field. The conclusions, grounded in real-world data, offer valuable insights and provide a theoretical foundation for merchants to optimize their livestreaming strategies.
Do Comments Drive Sales? Exploring the Impact of Real-Time Comments on Live Streaming E-Commerce Performance
Yaozong Li, Weina Lin, Yuanyue Feng# (# corresponding author)
Accepted by the Doctoral Consortium of the 24th Wuhan International Conference on E-Business (WHICEB) 2025-05-11
[Purpose/Significance] This study aims to deepen the understanding of how real-time comments influence sales performance in the context of live streaming e-commerce. [Methodology/Process] A theoretical framework was constructed based on the Elaboration Likelihood Model (ELM). Utilizing real data from 52 livestreams on the Douyin platform, two deep learning-based text classification models were trained and employed to conduct an in-depth analysis of comments. A generalized linear model (GLM) was then employed for empirical analysis. [Findings/Conclusion] It was found that comment length has a significantly negative effect on sales, whereas sentiments and product attention exhibit an inverted U-shaped relationship with sales, and the number of comments has a significantly positive impact on sales. The model demonstrated high explanatory power (Pseudo R² = 0.8249). [Originality/Value] Cutting-edge natural language processing (NLP) methods were applied to empirical studies in live streaming e-commerce, fostering interdisciplinary integration and advancing methodological innovation in the field. The conclusions, grounded in real-world data, offer valuable insights and provide a theoretical foundation for merchants to optimize their livestreaming strategies.
All publications
Selected Projects (view all )
Research on Sentiment Analysis of Live Streaming E-commerce Real-time Comments Based on RCGA-NET
This project focuses on the intersection of AI, management science and e-commerce, applying deep learning to live - streaming e - commerce. I proposed the RCGA - Net model to extract sentiment features and classify evaluation objects in comments. I collected real - world data from TikTok live - streaming rooms, including comments and sales figures, to analyze the impact of comment features on sales.
July 2024 – March 2025 · Deep Learning, Sentiment Analysis, Live Streaming E-commerce, Real-time Comments, Elaboration Likelihood Model
Research on Sentiment Analysis of Live Streaming E-commerce Real-time Comments Based on RCGA-NET
This project focuses on the intersection of AI, management science and e-commerce, applying deep learning to live - streaming e - commerce. I proposed the RCGA - Net model to extract sentiment features and classify evaluation objects in comments. I collected real - world data from TikTok live - streaming rooms, including comments and sales figures, to analyze the impact of comment features on sales.
July 2024 – March 2025 · Deep Learning, Sentiment Analysis, Live Streaming E-commerce, Real-time Comments, Elaboration Likelihood Model
Machine Learning-Based Pricing of Used Sailboats
This project aims to predict the listing prices of second-hand sailboats using machine learning methods, including Neural Networks, Random Forest, LGBM, and XGBoost. After data collection and preprocessing, model performance is evaluated through learning curve comparison, with LGBM chosen for its accuracy and good fit. The analysis focuses on regional effects on sailboat pricing, exploring variations by geographic area and model type. Additionally, a regional simulation model is developed to predict second-hand sailboat prices in Hong Kong based on economic and freight indicators. Statistical methods like paired sample T-tests are used to analyze pricing differences between monohulls and catamarans.
March 2023 – April 2023 · Machine Learning, Regression Analysis, Price Prediction, Second-Hand Sailboats
Machine Learning-Based Pricing of Used Sailboats
This project aims to predict the listing prices of second-hand sailboats using machine learning methods, including Neural Networks, Random Forest, LGBM, and XGBoost. After data collection and preprocessing, model performance is evaluated through learning curve comparison, with LGBM chosen for its accuracy and good fit. The analysis focuses on regional effects on sailboat pricing, exploring variations by geographic area and model type. Additionally, a regional simulation model is developed to predict second-hand sailboat prices in Hong Kong based on economic and freight indicators. Statistical methods like paired sample T-tests are used to analyze pricing differences between monohulls and catamarans.
March 2023 – April 2023 · Machine Learning, Regression Analysis, Price Prediction, Second-Hand Sailboats
Research on the Impact of AI Anchors’ Emotions on Consumers’ Purchase Intentions
This study employs scenario-based experiments to empirically analyze the theoretical mechanisms through which high-energy emotional expressions by AI anchors in live-streaming settings enhance consumers’ purchase intentions.
February 2023 – January 2024 · Live Streaming E-commerce, AI Anchors, Emotions
Research on the Impact of AI Anchors’ Emotions on Consumers’ Purchase Intentions
This study employs scenario-based experiments to empirically analyze the theoretical mechanisms through which high-energy emotional expressions by AI anchors in live-streaming settings enhance consumers’ purchase intentions.
February 2023 – January 2024 · Live Streaming E-commerce, AI Anchors, Emotions
A Grayscale-Simulated Jacquard Fabric Design Method with Information Hiding Function
This project, conducted in collaboration with a textile engineering research team, proposed a novel design method for Jacquard fabrics that integrates both grayscale image simulation and hidden information embedding. The technique utilizes combinations of English letters and Arabic numerals to encode visual patterns that are imperceptible to the human eye but embedded within the grayscale representation of the fabric.
May 2020 – November 2020 · OpenCV, Anti-counterfeiting Technology, Jacquard Fabric Design, Python, Cpp
A Grayscale-Simulated Jacquard Fabric Design Method with Information Hiding Function
This project, conducted in collaboration with a textile engineering research team, proposed a novel design method for Jacquard fabrics that integrates both grayscale image simulation and hidden information embedding. The technique utilizes combinations of English letters and Arabic numerals to encode visual patterns that are imperceptible to the human eye but embedded within the grayscale representation of the fabric.
May 2020 – November 2020 · OpenCV, Anti-counterfeiting Technology, Jacquard Fabric Design, Python, Cpp