Year: 2024
Type: article
Abstract: This study investigates a novel sentiment analysis model designed for the Xiaohongshu platform, leveraging the RoBERTa model combined with BiLSTM and attention mechanisms. Xiaohongshu, a prominent social commerce platform in China, offers unique challenges for sentiment analysis due to its user-generated content, which is often informal and multi-dimensional. The RoBERTa-BiLSTM-Attention model is introduced to capture complex semantic nuances and enhance the accuracy of sentiment classification. A comparative experiment was conducted to evaluate this model's effectiveness against traditional methods, including Word2vec-LSTM, Word2vec-BiLSTM, BERT-BiLSTM, and BERT-BiLSTM-Attention. Results show that the RoBERTa-BiLSTM-Attention model outperforms these models in terms of accuracy and F1-score, demonstrating its potential to capture and classify sentiment in user-generated content on Xiaohongshu with improved robustness and depth.
Author: Yidi Xie
Institution: Shanghai Ocean University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Carnegie Mellon University
OpenAlex ID: W4405592576