{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T15:38:32Z","timestamp":1779118712664,"version":"3.51.4"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Lt","issue":"07n08","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:p> Accurate analysis of targeted sentiment in online course reviews helps in understanding emotional changes of learners and improving the course quality. In this paper, we propose a fine-tuned bidirectional encoder representation from transformers (BERT) model for targeted sentiment analysis of course reviews. Specifically, it consists of two parts: binding corporate rules \u2014 conditional random field (BCR-CRF) target extraction model and a binding corporate rules \u2014 double attention (BCR-DA) target sentiment analysis model. Firstly, based on a large-scale Chinese review corpus, intra-domain unsupervised training of a BERT pre-trained model (BCR) is performed. Then, a Conditional Random Field (CRF) layer is introduced to add grammatical constraints to the output sequence of the semantic representation layer in the BCR model. Finally, a BCR-DA model containing double attention layers is constructed to express the sentiment polarity of the course review targets in a classified manner. Experiments are performed on Chinese online course review datasets of China MOOC. The experimental results show that the F1 score of the BCR-CRF model reaches above 92%, and the accuracy of the BCR-DA model reaches above 72%. <\/jats:p>","DOI":"10.1142\/s0218213020400187","type":"journal-article","created":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T04:24:36Z","timestamp":1606710276000},"page":"2040018","source":"Crossref","is-referenced-by-count":22,"title":["A BERT Fine-tuning Model for Targeted Sentiment Analysis of Chinese Online Course Reviews"],"prefix":"10.1142","volume":"29","author":[{"given":"Huibing","family":"Zhang","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junchao","family":"Dong","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Xi\u2019an Jiaotong University City College, Xi\u2019an, 710018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Bi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Xi\u2019an Jiaotong University City College, Xi\u2019an, 710018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,11,30]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213020400187","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T04:24:37Z","timestamp":1606710277000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213020400187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,30]]},"references-count":0,"journal-issue":{"issue":"07n08","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["10.1142\/S0218213020400187"],"URL":"https:\/\/doi.org\/10.1142\/s0218213020400187","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,30]]}}}