{"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/cross-lingual-sentiment-quantification","title":"Cross-Lingual Sentiment Quantification","arxiv_id":"1904.07965","date":"2019-04-16","proceeding":null,"authors":["Andrea Esuli","Alejandro Moreo","Fabrizio Sebastiani"],"abstract":"\\emph{Sentiment Quantification} (i.e., the task of estimating the relative frequency of sentiment-related classes -- such as \\textsf{Positive} and \\textsf{Negative} -- in a set of unlabelled documents) is an important topic in sentiment analysis, as the study of sentiment-related quantities and trends across a population is often of higher interest than the analysis of individual instances. In this work we propose a method for \\emph{Cross-Lingual Sentiment Quantification}, the task of performing sentiment quantification when training documents are available for a source language $\\mathcal{S}$ but not for the target language $\\mathcal{T}$ for which sentiment quantification needs to be performed. Cross-lingual sentiment quantification (and cross-lingual \\emph{text} quantification in general) has never been discussed before in the literature; we establish baseline results for the binary case by combining state-of-the-art quantification methods with methods capable of generating cross-lingual vectorial representations of the source and target documents involved. We present experimental results obtained on publicly available datasets for cross-lingual sentiment classification; the results show that the presented methods can perform cross-lingual sentiment quantification with a surprising level of accuracy.","url_abs":"https://arxiv.org/abs/1904.07965v2","url_pdf":"https://arxiv.org/pdf/1904.07965v2.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":"cross-lingual-sentiment-quantification","repo_url":"https://github.com/AlexMoreo/cl-quant","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"cross-lingual-sentiment-quantification","repo_url":"https://github.com/HLT-ISTI/cl-quant","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"cross-lingual-sentiment-quantification","repo_url":"https://github.com/AlexMoreo/pydci","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cross-lingual-sentiment-classification","task_name":"Cross-Lingual Sentiment Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}