{"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/sentihood-targeted-aspect-based-sentiment","title":"SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods","arxiv_id":"1610.03771","date":"2016-10-12","proceeding":"COLING 2016 12","authors":["Marzieh Saeidi","Guillaume Bouchard","Maria Liakata","Sebastian Riedel"],"abstract":"In this paper, we introduce the task of targeted aspect-based sentiment\nanalysis. The goal is to extract fine-grained information with respect to\nentities mentioned in user comments. This work extends both aspect-based\nsentiment analysis that assumes a single entity per document and targeted\nsentiment analysis that assumes a single sentiment towards a target entity. In\nparticular, we identify the sentiment towards each aspect of one or more\nentities. As a testbed for this task, we introduce the SentiHood dataset,\nextracted from a question answering (QA) platform where urban neighbourhoods\nare discussed by users. In this context units of text often mention several\naspects of one or more neighbourhoods. This is the first time that a generic\nsocial media platform in this case a QA platform, is used for fine-grained\nopinion mining. Text coming from QA platforms is far less constrained compared\nto text from review specific platforms which current datasets are based on. We\ndevelop several strong baselines, relying on logistic regression and\nstate-of-the-art recurrent neural networks.","url_abs":"http://arxiv.org/abs/1610.03771v1","url_pdf":"http://arxiv.org/pdf/1610.03771v1.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":[],"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":"opinion-mining","task_name":"Opinion Mining"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-sentihood","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"Sentihood","model":"LSTM-LOC","rank_in_archive_order":5,"of":5,"metrics":{"Aspect":"69.3","Sentiment":"81.9"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.03771","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}