GP‐FMLNet:A feature matrix learning network enhanced by glyph and phonetic information for Chinese sentiment analysis  

作　　者：Jing Li Dezheng Zhang Yonghong Xie Aziguli Wulamu Yao Zhang 

机构地区：[1]School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing,China [2]Beijing Key Laboratory of Knowledge Engineering for Materials Science,University of Science and Technology Beijing,Beijing,China [3]University of Alberta,Edmonton,Alberta,Canada

出　　处：《CAAI Transactions on Intelligence Technology》2024年第4期960-972,共13页智能技术学报（英文）

基　　金：Science and Technology Innovation 2030‐“New Generation Artificial Intelligence”major project,Grant/Award Number:2020AAA0108703。

摘　　要：Sentiment analysis is a fine‐grained analysis task that aims to identify the sentiment polarity of a specified sentence.Existing methods in Chinese sentiment analysis tasks only consider sentiment features from a single pole and scale and thus cannot fully exploit and utilise sentiment feature information,making their performance less than ideal.To resolve the problem,the authors propose a new method,GP‐FMLNet,that integrates both glyph and phonetic information and design a novel feature matrix learning process for phonetic features with which to model words that have the same pinyin information but different glyph information.Our method solves the problem of misspelling words influencing sentiment polarity prediction results.Specifically,the authors iteratively mine character,glyph,and pinyin features from the input comments sentences.Then,the authors use soft attention and matrix compound modules to model the phonetic features,which empowers their model to keep on zeroing in on the dynamic‐setting words in various positions and to dispense with the impacts of the deceptive‐setting ones.Ex-periments on six public datasets prove that the proposed model fully utilises the glyph and phonetic information and improves on the performance of existing Chinese senti-ment analysis algorithms.

关 键 词：aspect‐level sentiment analysis deep learning feature extraction glyph and phonetic feature matrix compound learning 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]