{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:22:15Z","timestamp":1767183735064},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Sentiment classification is a fundamental task in NLP. However, as revealed by many researches, sentiment classification models are highly domain-dependent. It is worth investigating to leverage data from different domains to improve the classification performance in each domain. In this work, we propose a novel completely-shared multi-domain neural sentiment classification model to learn domain-aware word embeddings and make use of domain-aware attention mechanism. Our model first utilizes BiLSTM for domain classification and extracts domain-specific features for words, which are then combined with general word embeddings to form domain-aware word embeddings. Domain-aware word embeddings are fed into another BiLSTM to extract sentence features. The domain-aware attention mechanism is used for selecting significant features, by using the domain-aware sentence representation as the query vector. Evaluation results on public datasets with 16 different domains demonstrate the efficacy of our proposed model. Further experiments show the generalization ability and the transferability of our model.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/681","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"4904-4910","source":"Crossref","is-referenced-by-count":19,"title":["Multi-Domain Sentiment Classification Based on Domain-Aware Embedding and Attention"],"prefix":"10.24963","author":[{"given":"Yitao","family":"Cai","sequence":"first","affiliation":[{"name":"Institute of Computer Science and Technology, Peking University"},{"name":"The MOE Key Laboratory of Computational Linguistics, Peking University"},{"name":"Center for Data Science, Peking University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Wan","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Technology, Peking University"},{"name":"The MOE Key Laboratory of Computational Linguistics, Peking University"},{"name":"Center for Data Science, Peking University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:51:01Z","timestamp":1564300261000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/681"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/681","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}