{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T15:28:42Z","timestamp":1770046122117,"version":"3.49.0"},"reference-count":14,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,4,22]]},"abstract":"<jats:p>Aiming at the problem that the Aspect-based sentiment analysis in Chinese has low recognition rate due to many steps, this paper proposes an improved BiLSTM-CRF model based on combine the Chinese character vector and Chinese words position feature, which can extract attribute words and sentiment words jointly simultaneously, while extracting Polarity judges of sentiment words. Experiments show that the improved model improves the precision rate by 9.2% \u00a013.32%, recall rate improves 0.48% \u00a021.29%, F-measure improves 7.33% \u00a015.74% compared with Conditional Random Fields (CRF) model and Long Short Term Memory (LSTM) model on the self-built 6357 mobile reviews dataset. The experimental results show that the model improves the accuracy of Aspect-based sentiment analysis and can effectively obtain the information required by users need in evaluation texts.<\/jats:p>","DOI":"10.3233\/jifs-192078","type":"journal-article","created":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T11:46:59Z","timestamp":1613476019000},"page":"8697-8707","source":"Crossref","is-referenced-by-count":16,"title":["Aspect-based sentiment analysis in Chinese based on mobile reviews for BiLSTM-CRF"],"prefix":"10.1177","volume":"40","author":[{"given":"Ya Lin","family":"Miao","sequence":"first","affiliation":[{"name":"Department of Information Science, Xi\u2019an University of Technology, Xi\u2019an Shaanxi, China"}]},{"given":"Wen Fang","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Information Science, Xi\u2019an University of Technology, Xi\u2019an Shaanxi, China"}]},{"given":"Yi Chun","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Information Science, Xi\u2019an University of Technology, Xi\u2019an Shaanxi, China"}]},{"given":"Shun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Information Science, Xi\u2019an University of Technology, Xi\u2019an Shaanxi, China"}]},{"given":"Yan Long","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Information Science, Xi\u2019an University of Technology, Xi\u2019an Shaanxi, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-192078_ref2","doi-asserted-by":"crossref","unstructured":"Liu B. , Sentiment analysis: Mining opinions, sentiments, and emotions[M], Cambridge University Press, 2015.","DOI":"10.1017\/CBO9781139084789"},{"issue":"7","key":"10.3233\/JIFS-192078_ref3","first-page":"167","article-title":"Overview of Aspect-based opinion mining research on network product reviews[J]","volume":"325","author":"Duanwu","year":"2018","journal-title":"Modern Information"},{"issue":"1","key":"10.3233\/JIFS-192078_ref4","first-page":"144","article-title":"The Impact of Internet Word of Mouth on Product Sales: Based on Aspect-based sentiment analysis Method[J]","volume":"29","author":"Yuan","year":"2017","journal-title":"Management Review"},{"issue":"3","key":"10.3233\/JIFS-192078_ref5","first-page":"617","article-title":"Evaluation object-emotional word pair extraction based on semantic analysis[J]","volume":"40","author":"Tengzhen","year":"2017","journal-title":"Chinese Journal of Computers"},{"issue":"09","key":"10.3233\/JIFS-192078_ref6","first-page":"123","article-title":"Fine-grained sentiment analysis based on cyclic entity network[J]","volume":"33","author":"Chuan","year":"2019","journal-title":"Chinese Journal of Information"},{"key":"10.3233\/JIFS-192078_ref7","unstructured":"Wei J. and Ho H.H. , A novel lexicalized HMM-based learning framework for web opinion mining[C], International Conference on Machine Learning, ACM, 2009, 465\u2013472."},{"issue":"10","key":"10.3233\/JIFS-192078_ref12","first-page":"100","article-title":"Multi-attention hierarchical neural network for text sentiment analysis[J]","volume":"56","author":"Hu","year":"2020","journal-title":"Computer Engineering and Applications"},{"key":"10.3233\/JIFS-192078_ref13","doi-asserted-by":"crossref","unstructured":"Bao L. , Lambert P. and Badia T. , Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis[C], Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics:Student ResearchWorkshop, ACL, 2019, 253\u2013259.","DOI":"10.18653\/v1\/P19-2035"},{"key":"10.3233\/JIFS-192078_ref14","doi-asserted-by":"crossref","unstructured":"Hu M. , Peng Y. , Huang Z. , et al., Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification[C], Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL, 2019, 537\u2013546.","DOI":"10.18653\/v1\/P19-1051"},{"key":"10.3233\/JIFS-192078_ref15","doi-asserted-by":"crossref","unstructured":"Liang B. , Du J. , Xu R. , et al., Context-aware Embedding for Targeted Aspect-based Sentiment Analysis[C], Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL, 2019.","DOI":"10.18653\/v1\/P19-1462"},{"key":"10.3233\/JIFS-192078_ref16","doi-asserted-by":"crossref","unstructured":"Ma D. , Li S. and Wang H. , Joint Learning for Targeted Sentiment Analysis[C], Conference on Empirical Methods in Natural Language Processing, EMNLP, 2018.","DOI":"10.18653\/v1\/D18-1504"},{"issue":"2-3","key":"10.3233\/JIFS-192078_ref17","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s10579-005-7880-9","article-title":"Annotating expressions of opinions and emotions in language[J]","volume":"39","author":"Wiebe","year":"2005","journal-title":"Language Resources and Evaluation"},{"key":"10.3233\/JIFS-192078_ref18","doi-asserted-by":"crossref","unstructured":"Hu M. and Liu B. , Mining and summarizing customer reviews[C], Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2004, 168\u2013177.","DOI":"10.1145\/1014052.1014073"},{"issue":"8","key":"10.3233\/JIFS-192078_ref22","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory[J]","volume":"9","author":"Hochreiter","journal-title":"{Neural Computation"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-192078","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T03:54:19Z","timestamp":1770004459000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-192078"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,22]]},"references-count":14,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-192078","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,22]]}}}