Using Informative Score for Instance Selection Strategy in Semi-Supervised Sentiment Classification  

作　　者：Vivian Lee Lay Shan Gan Keng Hoon Tan Tien Ping Rosni Abdullah 

机构地区：[1]School of Computer Sciences,Universiti Sains Malaysia,Pulau Pinang,11800,Malaysia

出　　处：《Computers, Materials & Continua》2023年第3期4801-4818,共18页计算机、材料和连续体（英文）

基　　金：This research is supported by Fundamental Research Grant Scheme(FRGS),Ministry of Education Malaysia(MOE)under the project code,FRGS/1/2018/ICT02/USM/02/9 titled,Automated Big Data Annotation for Training Semi-Supervised Deep Learning Model in Sentiment Classification.

摘　　要：Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service.Existing studies heavily rely on sentiment classification methods that require fully annotated inputs.However,there is limited labelled text available,making the acquirement process of the fully annotated input costly and labour-intensive.Lately,semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods.Nevertheless,some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training.Literature also shows that not all unlabelled instances are equally useful;thus identifying the informative unlabelled instances is beneficial in training a semi-supervised model.To achieve this,an informative score is proposed and incorporated into semisupervised sentiment classification.The evaluation is performed on a semisupervised method without an informative score and with an informative score.By using the informative score in the instance selection strategy to identify informative unlabelled instances,semi-supervised models perform better compared to models that do not incorporate informative scores into their training.Although the performance of semi-supervised models incorporated with an informative score is not able to surpass the supervised models,the results are still found promising as the differences in performance are subtle with a small difference of 2%to 5%,but the number of labelled instances used is greatly reduced from100%to 40%.The best finding of the proposed instance selection strategy is achieved when incorporating an informative score with a baseline confidence score at a 0.5:0.5 ratio using only 40%labelled data.

关 键 词：Document-level sentiment classification semi-supervised learning instance selection informative score 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]