{"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/sentiment-analysis-on-financial-news","title":"Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation","arxiv_id":"1707.09448","date":"2017-07-29","proceeding":null,"authors":["Vineet John","Olga Vechtomova"],"abstract":"This paper discusses the approach taken by the UWaterloo team to arrive at a\nsolution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of\nSemEval 2017. The paper describes the document vectorization and sentiment\nscore prediction techniques used, as well as the design and implementation\ndecisions taken while building the system for this task. The system uses text\nvectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled\nwith regression model variants to predict the sentiment scores. Amongst the\nmethods examined, unigrams and bigrams coupled with simple linear regression\nobtained the best baseline accuracy. The paper also explores data augmentation\nmethods to supplement the training dataset. This system was designed for\nSubtask 2 (News Statements and Headlines).","url_abs":"http://arxiv.org/abs/1707.09448v1","url_pdf":"http://arxiv.org/pdf/1707.09448v1.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":"sentiment-analysis-on-financial-news","repo_url":"https://github.com/v1n337/semeval2017-task5","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}