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FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings., , , and . SemEval@ACL, page 904-908. Association for Computational Linguistics, (2017)Addressing Bias and Fairness in Machine Learning: A Practical Guide and Hands-on Tutorial., , , and . KDD, page 5779-5780. ACM, (2023)Entity-Relationship Search over the Web., , , and . CoRR, (2018)Sentiment Aggregate Functions for Political Opinion Polling using Microblog Streams., , and . C3S2E, page 44-50. ACM, (2016)LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering., , and . ICAIF, page 130-138. ACM, (2022)Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints., , , , , , , and . Trans. Mach. Learn. Res., (2024)FairGBM: Gradient Boosting with Fairness Constraints., , , , and . CoRR, (2022)SentiBubbles: Topic Modeling and Sentiment Visualization of Entity-centric Tweets., , , and . C3S2E, page 123-124. ACM, (2016)Learning Word Embeddings from the Portuguese Twitter Stream: A Study of Some Practical Aspects., , , , and . EPIA, volume 10423 of Lecture Notes in Computer Science, page 880-891. Springer, (2017)Entity Retrieval and Text Mining for Online Reputation Monitoring.. CoRR, (2018)