Title: Sentiment classification method of fresh agricultural product reviews based on semantic and emotional optimisation

Authors: Yindong Dong; Yu Zhang; Guodong Wu; Xia Chen; Lijing Tu; Guohua Fan

Addresses: School of Information and Artificial Intelligence, Anhui Agricultural University, 130 Changjiang West Road, Hefei City, Anhui Province, 230036, China ' School of Information and Artificial Intelligence, Anhui Agricultural University, 130 Changjiang West Road, Hefei City, Anhui Province, 230036, China ' School of Information and Artificial Intelligence, Anhui Agricultural University, 130 Changjiang West Road, Hefei City, Anhui Province, 230036, China ' School of Information and Artificial Intelligence, Anhui Agricultural University, 130 Changjiang West Road, Hefei City, Anhui Province, 230036, China ' School of Information and Artificial Intelligence, Anhui Agricultural University, 130 Changjiang West Road, Hefei City, Anhui Province, 230036, China ' School of Information and Artificial Intelligence, Anhui Agricultural University, 130 Changjiang West Road, Hefei City, Anhui Province, 230036, China

Abstract: The complexity of semantics and structure in fresh agricultural product reviews can lead to sparsity in the distribution of text sentiment information. To address this issue, a sentiment classification model (Electra XLNet BIGRU multi-head attention - EXBMA) for fresh agricultural product reviews is proposed, which combines semantic and sentiment information optimisation. Firstly, the Electra model and XLNet model were used to obtain word level and sentence level information of fresh agricultural product reviews, respectively, and the semantic features obtained were fused using (multimodal compact bilinear - MCB) algorithm. Secondly, TextRank is used to extract keywords to construct an emotional key dictionary and combined with a multi-head attention mechanism to enhance emotional attention. Finally, to enhance the contextual representation, the BIGRU is used to learn contextual information to improve the classification performance. The experimental results indicate that the EXBMA can better achieve collaborative optimisation of semantic and emotional information, and performs better than other existing classification models in emotional classification of fresh agricultural product reviews.

Keywords: fresh agricultural products; emotional classification; semantic optimisation; sentiment lexicon; attention mechanism.

DOI: 10.1504/IJIIDS.2025.143485

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.1, pp.32 - 56

Received: 28 Feb 2024
Accepted: 04 Jun 2024
Published online: 23 Dec 2024 *