乐胖代购免代理版

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Its utility in the business domain is undeniable, offering actionable insights into customer opinions and attitudes, empowering data-driven decisions that enhance products, services, and customer satisfaction. The expansion of Sentiment Analysis into the financial sector came as a direct consequence, prompting the adaptation of powerful Natural Language Processing models to these contexts. In this study, we rigorously test numerous classical Machine Learning classification algorithms and ensembles against five contemporary Deep Learning Pre-Trained models, like BERT, RoBERTa, and three variants of FinBERT. However, its aim extends beyond evaluating the performance of modern methods, especially those designed for financial tasks, to a comparison of them with classical ones. We also explore how different text representation and data augmentation techniques impact classification outcomes when classical methods are employed. The study yields a wealth of intriguing results, which are thoroughly discussed.<\/jats:p>","DOI":"10.3233\/idt-230478","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T16:23:13Z","timestamp":1699978993000},"page":"893-915","source":"Crossref","is-referenced-by-count":1,"title":["Financial sentiment analysis: Classic methods vs. deep learning models"],"prefix":"10.1177","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4226-6597","authenticated-orcid":false,"given":"Aikaterini","family":"Karanikola","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5345-0724","authenticated-orcid":false,"given":"Gregory","family":"Davrazos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4717-031X","authenticated-orcid":false,"given":"Charalampos M.","family":"Liapis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2247-3082","authenticated-orcid":false,"given":"Sotiris","family":"Kotsiantis","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/IDT-230478_ref1","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1007\/978-1-4899-7687-1_907","article-title":"Sentiment Analysis and Opinion Mining","volume":"1","author":"Zhang","year":"2017","journal-title":"Encyclopedia of Machine Learning and Data Mining."},{"key":"10.3233\/IDT-230478_ref2","first-page":"3522","article-title":"Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning","volume":"82","author":"Yang","year":"2020","journal-title":"IEEE Access."},{"issue":"5","key":"10.3233\/IDT-230478_ref3","doi-asserted-by":"crossref","first-page":"109","DOI":"10.9781\/ijimai.2018.12.005","article-title":"Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method","volume":"5","author":"Harish","year":"2019","journal-title":"International Journal of Interactive Multimedia and Artificial Intelligence."},{"issue":"2","key":"10.3233\/IDT-230478_ref4","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1007\/s42452-019-1926-x","article-title":"Sentiment analysis on IMDB using lexicon and neural networks","volume":"2","author":"Shaukat","year":"2020","journal-title":"SN Applied Sciences."},{"key":"10.3233\/IDT-230478_ref5","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.chb.2013.05.024","article-title":"Sentiment analysis in Facebook and its application to e-learning","volume":"31","author":"Ortigosa","year":"2014","journal-title":"Computers in Human Behavior."},{"key":"10.3233\/IDT-230478_ref6","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1080\/13645579.2017.1381821","article-title":"Social media analytics for YouTube comments: potential and limitations","volume":"21","author":"Thelwall","year":"2018","journal-title":"International Journal of Social Research Methodology."},{"key":"10.3233\/IDT-230478_ref7","doi-asserted-by":"crossref","first-page":"23253","DOI":"10.1109\/ACCESS.2017.2776930","article-title":"Deep Convolution Neural Networks for Twitter Sentiment Analysis","volume":"6","author":"Jianqiang","year":"2018","journal-title":"IEEE Access."},{"key":"10.3233\/IDT-230478_ref8","doi-asserted-by":"crossref","unstructured":"Zimbra D, Abbasi A, Zeng D. 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