{"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/feup-at-semeval-2017-task-5-predicting","title":"FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings","arxiv_id":"1704.05091","date":"2017-04-17","proceeding":"SEMEVAL 2017 8","authors":["Pedro Saleiro","Eduarda Mendes Rodrigues","Carlos Soares","Eugénio Oliveira"],"abstract":"This paper presents the approach developed at the Faculty of Engineering of\nUniversity of Porto, to participate in SemEval 2017, Task 5: Fine-grained\nSentiment Analysis on Financial Microblogs and News. The task consisted in\npredicting a real continuous variable from -1.0 to +1.0 representing the\npolarity and intensity of sentiment concerning companies/stocks mentioned in\nshort texts. We modeled the task as a regression analysis problem and combined\ntraditional techniques such as pre-processing short texts, bag-of-words\nrepresentations and lexical-based features with enhanced financial specific\nbag-of-embeddings. We used an external collection of tweets and news headlines\nmentioning companies/stocks from S\\&P 500 to create financial word embeddings\nwhich are able to capture domain-specific syntactic and semantic similarities.\nThe resulting approach obtained a cosine similarity score of 0.69 in sub-task\n5.1 - Microblogs and 0.68 in sub-task 5.2 - News Headlines.","url_abs":"http://arxiv.org/abs/1704.05091v1","url_pdf":"http://arxiv.org/pdf/1704.05091v1.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":"feup-at-semeval-2017-task-5-predicting","repo_url":"https://github.com/saleiro/Financial-Sentiment-Analysis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"feup-at-semeval-2017-task-5-predicting","repo_url":"https://github.com/saleiro/SemEval2017-Task5","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"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}