@inproceedings{c321b87bc7a642958b7c4c1008cbb1c9,
title = "Research on Sentiment Classification Based on Hybrid BERT Model and Hypergraph Attention Network",
abstract = "With the rapid growth of social media comments, Sentiment Analysis (SA), as a part of Natural Language Processing (NLP), faces an urgent need for efficient analysis and extraction of vast amounts of internet data. This study proposes a sentiment classification model based on a hybrid BERT model and a hypergraph attention network (HB\_HAGT), aiming to accurately identify and classify textual sentiments by combining BERT's semantic representation capabilities with the local attention mechanism of the hypergraph attention network. By constructing a hypergraph model to enrich the feature representation of the data, the model demonstrates its superiority in sentiment analysis tasks through comparative experiments and ablation studies across multiple datasets.",
keywords = "Hybrid BERT Model, Hypergraph Attention Network, Sentiment Analysis",
author = "Wei Dai and Dequan Zheng and Feng Yu and Feng Yan",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 17th International Conference on Machine Learning and Computing, ICMLC 2025 ; Conference date: 14-02-2025 Through 17-02-2025",
year = "2025",
doi = "10.1007/978-3-031-94892-3\_25",
language = "英语",
isbn = "9783031948916",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "339--349",
editor = "Lin Huang and David Greenhalgh",
booktitle = "Proceedings of 17th International Conference on Machine Learning and Computing - ICMLC 2025",
address = "德国",
}