{"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/sa2sl-from-aspect-based-sentiment-analysis-to","title":"SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence","arxiv_id":"2105.15079","date":"2021-05-31","proceeding":null,"authors":["Luong Luc Phan","Phuc Huynh Pham","Kim Thi-Thanh Nguyen","Tham Thi Nguyen","Sieu Khai Huynh","Luan Thanh Nguyen","Tin Van Huynh","Kiet Van Nguyen"],"abstract":"In this paper, we present a process of building a social listening system based on aspect-based sentiment analysis in Vietnamese from creating a dataset to building a real application. Firstly, we create UIT-ViSFD, a Vietnamese Smartphone Feedback Dataset as a new benchmark corpus built based on a strict annotation schemes for evaluating aspect-based sentiment analysis, consisting of 11,122 human-annotated comments for mobile e-commerce, which is freely available for research purposes. We also present a proposed approach based on the Bi-LSTM architecture with the fastText word embeddings for the Vietnamese aspect based sentiment task. Our experiments show that our approach achieves the best performances with the F1-score of 84.48% for the aspect task and 63.06% for the sentiment task, which performs several conventional machine learning and deep learning systems. Last but not least, we build SA2SL, a social listening system based on the best performance model on our dataset, which will inspire more social listening systems in future.","url_abs":"https://arxiv.org/abs/2105.15079v2","url_pdf":"https://arxiv.org/pdf/2105.15079v2.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":"sa2sl-from-aspect-based-sentiment-analysis-to","repo_url":"https://github.com/LuongPhan/UIT-ViSFD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","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":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"vietnamese-aspect-based-sentiment-analysis","task_name":"Vietnamese Aspect-Based Sentiment Analysis"},{"task_slug":"vietnamese-datasets","task_name":"Vietnamese Datasets"},{"task_slug":"vietnamese-sentiment-analysis","task_name":"Vietnamese Sentiment Analysis"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"fasttext","method_name":"fastText"}],"datasets_introduced":[{"slug":"uit-visfd","name":"UIT-ViSFD","full_name":"Vietnamese \bAspect-Based Sentiment Analysis Dataset"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}