{"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/multilingual-aspect-clustering-for-sentiment","title":"Multilingual aspect clustering for sentiment analysis","arxiv_id":null,"date":"2019-12-09","proceeding":null,"authors":["Lucas Rafael Costella Pessutto","Danny Suarez Vargas","Viviane P. Moreira"],"abstract":"In the last few years, there has been growing interest in aspect-based sentiment analysis, which\r\ndeals with extracting, clustering, and rating the overall opinion about the features of the entity being\r\nevaluated. Techniques for aspect extraction can produce an undesirably large number of aspects —\r\nwith many of those relating to the same product feature. Hence, aspect clustering becomes necessary.\r\nCurrent solutions for aspect clustering are monolingual, but in many practical situations, reviews for\r\na given entity are available in several languages, calling for multilingual integration. In this article,\r\nwe address the novel task of multilingual aspect clustering, which aims at grouping semantically\r\nrelated aspects extracted from reviews written in several languages. Our method is unsupervised\r\nand relies on the contextual information of the aspects, which is represented by word embeddings.\r\nThis representation allied with a suitable similarity measure allows clustering related aspects. Our\r\nexperiments on two datasets with five languages each showed that our unsupervised clustering\r\ntechnique achieves results that outperform monolingual baselines adapted to work with multilingual\r\ndata. We also show the benefits of the multilingual approach compared to using languages in isolation.","url_abs":"https://www.sciencedirect.com/science/article/pii/S0950705119306070","url_pdf":"https://reader.elsevier.com/reader/sd/pii/S0950705119306070?token=D068731FAF7DBD05F21054BE3F5A42CC93310D6AB58A0880E3548E6CE5EC922B7390C7D7DB891F425716C3F42AECA838&originRegion=eu-west-1&originCreation=20210415095315","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":"multilingual-aspect-clustering-for-sentiment","repo_url":"https://github.com/lucasrafaelc/Multilingual-Aspect-Clustering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"aspect-extraction","task_name":"Aspect Extraction"},{"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":"clustering","task_name":"Clustering"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}