{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:02:26Z","timestamp":1769832146303,"version":"3.49.0"},"reference-count":8,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,2,2]]},"abstract":"<jats:p>The purpose of aspect-level sentiment analysis is to identify the contextual sentence expressions given by sentiment for some aspects. For previous works, many scholars have proved the importance of the interaction between aspects and contexts. However, most existing methods ignore or do not specifically capture the position information of the aspect targets in the sentence. Thus, we propose an aspect-level sentiment analysis based on joint aspect and position hierarchy attention mechanism network. At the same time, the model adopts a joint approach to make the model of the aspect features and the position features. On the one hand, this method clearly captures the interaction between aspect words and context when inputting word vector information. On the other hand, this method can enhance the importance of position information in the sentence and boost the information retrieval ability of the model. Additionally, the model utilizes a hierarchical attention mechanism to extract feature information and to differentiate sentiment towards, which is similar to filtering useless information again. Experiment on the SemEval 2014 dataset represent that our model achieves better performance on aspect-level sentiment classification.<\/jats:p>","DOI":"10.3233\/jifs-211515","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T12:20:20Z","timestamp":1634646020000},"page":"2207-2218","source":"Crossref","is-referenced-by-count":4,"title":["Aspect-level sentiment analysis for based on joint aspect and position hierarchy attention mechanism network"],"prefix":"10.1177","volume":"42","author":[{"given":"Dangguo","family":"Shao","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technoloy, Kunming, China"}]},{"given":"Qing","family":"An","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Kun","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Yan","family":"Xiang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technoloy, Kunming, China"}]},{"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technoloy, Kunming, China"}]},{"given":"Junjun","family":"Guo","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technoloy, Kunming, China"}]},{"given":"Runda","family":"Yin","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-211515_ref1","first-page":"87","article-title":"A novel hierarchical attention-based method for aspect-level sentiment classification","volume":"9","author":"Lakizadeh","year":"2021","journal-title":"Journal of AI and data mining"},{"key":"10.3233\/JIFS-211515_ref14","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vectormachine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural processing letters"},{"key":"10.3233\/JIFS-211515_ref16","first-page":"e1253","article-title":"Deep learning for sentiment analysis: A survey","volume":"8","author":"Zhang","year":"2018","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"10.3233\/JIFS-211515_ref23","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural computation"},{"key":"10.3233\/JIFS-211515_ref24","doi-asserted-by":"crossref","first-page":"104868","DOI":"10.1016\/j.knosys.2019.104868","article-title":"Aspect-basedsentiment analysis via fusing multiple sources of textual knowledge","volume":"183","author":"Wu","year":"2019","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/JIFS-211515_ref26","doi-asserted-by":"crossref","unstructured":"Joachims T. 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