{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:06:42Z","timestamp":1710263202760},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,15]]},"abstract":"<jats:p>With the booming development of E-commerce platforms in many counties, there is a massive amount of customers\u2019 review data in different products and services. Understanding customers\u2019 feedbacks in both current and new products can give online retailers the possibility to improve the product quality, meet customers\u2019 expectations, and increase the corresponding revenue. In this paper, we investigate the Vietnamese sentiment classification problem on two datasets containing Vietnamese customers\u2019 reviews. We propose eight different approaches, including Bi-LSTM, Bi-LSTM + Attention, Bi-GRU, Bi-GRU + Attention, Recurrent CNN, Residual CNN, Transformer, and PhoBERT, and conduct all experiments on two datasets, AIVIVN 2019 and our dataset self-collected from multiple Vietnamese e-commerce websites. The experimental results show that all our proposed methods outperform the winning solution of the competition \u201cAIVIVN 2019 Sentiment Champion\u201d with a significant margin. Especially, Recurrent CNN has the best performance in comparison with other algorithms in terms of both AUC (98.48%) and F1-score (93.42%) in this competition dataset and also surpasses other techniques in our dataset collected. Finally, we aim to publish our codes, and these two data-sets later to contribute to the current research community related to the field of sentiment analysis.<\/jats:p>","DOI":"10.3233\/faia200579","type":"book-chapter","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T23:12:19Z","timestamp":1600297939000},"source":"Crossref","is-referenced-by-count":3,"title":["An Efficient Framework for Vietnamese Sentiment Classification"],"prefix":"10.3233","author":[{"given":"Cuong V.","family":"Nguyen","sequence":"first","affiliation":[{"name":"AISIA Research Lab"}]},{"given":"Khiem H.","family":"Le","sequence":"additional","affiliation":[{"name":"AISIA Research Lab"}]},{"given":"Anh M.","family":"Tran","sequence":"additional","affiliation":[{"name":"AISIA Research Lab"}]},{"given":"Binh T.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"AISIA Research Lab"},{"name":"University of Science, Ho Chi Minh City, Vietnam"},{"name":"Vietnam National University in Ho Chi Minh City, Vietnam"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200579","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T13:40:48Z","timestamp":1600350048000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200579"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200579","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,15]]}}}