{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-hybrid-approach-for-aspect-based-sentiment","title":"A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models","arxiv_id":null,"date":"2019-03-04","proceeding":"ESWC 2019","authors":["Olaf Wallaart; Flavius Frasincar"],"abstract":"This work focuses on sentence-level aspect-based sentiment\r\nanalysis for restaurant reviews. A two-stage sentiment analysis algorithm\r\nis proposed. In this method, first a lexicalized domain ontology is used to\r\npredict the sentiment and as a back-up algorithm a neural network with\r\na rotatory attention mechanism (LCR-Rot) is utilized. Furthermore, two\r\nfeatures are added to the backup algorithm. The first extension changes\r\nthe order in which the rotatory attention mechanism operates (LCRRot-inv). The second extension runs over the rotatory attention mechanism for multiple iterations (LCR-Rot-hop). Using the SemEval-2015\r\nand SemEval-2016 data, we conclude that the two-stage method outperforms the baseline methods, albeit with a small percentage. Moreover,\r\nwe find that the method where we iterate multiple times over a rotatory\r\nattention mechanism has the best performance.","url_abs":"https://personal.eur.nl/frasincar/papers/ESWC2019/eswc2019.pdf","url_pdf":"https://personal.eur.nl/frasincar/papers/ESWC2019/eswc2019.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-hybrid-approach-for-aspect-based-sentiment","repo_url":"https://github.com/ofwallaart/HAABSA","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"aspect-based-sentiment-analysis-1","task_name":"Aspect-Based Sentiment Analysis"},{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval-1","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval 2015 Task 12","model":"HAABSA","rank_in_archive_order":2,"of":2,"metrics":{"Restaurant (Acc)":"80.6"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}