{"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/stars-are-all-you-need-a-distantly-supervised","title":"From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision","arxiv_id":"2305.01710","date":"2023-05-02","proceeding":null,"authors":["Wenchang Li","John P. Lalor","Yixing Chen","Vamsi K. Kanuri"],"abstract":"Sentiment analysis is integral to understanding the voice of the customer and informing businesses' strategic decisions. Conventional sentiment analysis involves three separate tasks: aspect-category detection (ACD), aspect-category sentiment analysis (ACSA), and rating prediction (RP). However, independently tackling these tasks can overlook their interdependencies and often requires expensive, fine-grained annotations. This paper introduces Unified Sentiment Analysis (Uni-SA), a novel learning paradigm that unifies ACD, ACSA, and RP into a coherent framework. To achieve this, we propose the Distantly Supervised Pyramid Network (DSPN), which employs a pyramid structure to capture sentiment at word, aspect, and document levels in a hierarchical manner. Evaluations on multi-aspect review datasets in English and Chinese show that DSPN, using only star rating labels for supervision, demonstrates significant efficiency advantages while performing comparably well to a variety of benchmark models. Additionally, DSPN's pyramid structure enables the interpretability of its outputs. Our findings validate DSPN's effectiveness and efficiency, establishing a robust, resource-efficient, unified framework for sentiment analysis.","url_abs":"https://arxiv.org/abs/2305.01710v3","url_pdf":"https://arxiv.org/pdf/2305.01710v3.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":"stars-are-all-you-need-a-distantly-supervised","repo_url":"https://github.com/nd-ball/dspn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"aspect-category-detection","task_name":"Aspect Category Detection"},{"task_slug":"aspect-category-sentiment-analysis","task_name":"Aspect Category Sentiment Analysis"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}