Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
|
|
---|---|---|
Article Number | 03018 | |
Number of page(s) | 11 | |
Section | Image Processing and Computer Vision | |
DOI | https://doi.org/10.1051/itmconf/20257003018 | |
Published online | 23 January 2025 |
Sentiment Analysis of Mobile Phone Reviews Using XGBoost and Word Vectors
Dalian University of Technology, Dalian, Liaoning, 116000, China
Consumer reviews are an important source of data used to judge and examine consumer sentiment, and data mining for reviews of electronic products is an important way to help improve the design of electronic products. The research is based on the consumer reviews of online cell phone e-commerce, The paper constructs a sentiment dictionary in this field based on the Sentiment Oriented Point Mutual Information (SO-PMI) algorithm, and the sentiment weight of the review word vectors. An extreme Gradient Boosting Tree (XGBoost) is used to integrate word vectors and a Large Language Model (LLM) to construct a sentiment recognition model, and finally, a review sentiment index is derived, which unfolds from multiple dimensions to analyze the sentiment tendency in consumer reviews. The empirical analysis shows that the accuracy, recall, area under the curve (AUC), and other validation indexes of the constructed sentiment recognition model are further improved compared with the LLM model, which has a certain application value. When applying the weighted word vector method, the model has been significantly improved compared with the LLM model, the accuracy is increased by 5%, the accuracy is increased by 10%, and the comprehensive accuracy is increased by 2% after the comprehensive application of the two.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.