{"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/financial-aspect-and-sentiment-predictions","title":"Financial Aspect and Sentiment Predictions with Deep Neural Networks: An Ensemble Approach","arxiv_id":null,"date":"2018-04-01","proceeding":null,"authors":["Guangyuan Piao; John G. Breslin"],"abstract":"In this paper, we describe our ensemble approach for sentiment\r\nand aspect predictions in the financial domain for a given text. This\r\nensemble approach uses Convolutional Neural Networks (CNNs)\r\nand Recurrent Neural Networks (RNNs) with a ridge regression\r\nand a voting strategy for sentiment and aspect predictions, and\r\ntherefore, does not rely on any handcrafted feature. Based on 5-cross\r\nvalidation on the released training set, the results show that CNNs\r\noverall perform better than RNNs on both tasks, and the ensemble\r\napproach can boost the performance further by leveraging different\r\ntypes of deep learning approaches.","url_abs":"https://www.researchgate.net/publication/324630980_Financial_Aspect_and_Sentiment_Predictions_with_Deep_Neural_Networks_An_Ensemble_Approach","url_pdf":"https://www.researchgate.net/publication/324630980_Financial_Aspect_and_Sentiment_Predictions_with_Deep_Neural_Networks_An_Ensemble_Approach","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":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-fiqa","task":"Sentiment Analysis","dataset":"FiQA","model":"Deep Neural Networks (DNN)","rank_in_archive_order":4,"of":4,"metrics":{"MSE":"0.09","R^2":"0.41"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}