Mammadov, AlisahibBakal, Gokhan2025-09-252025-09-2520249798350365887https://doi.org/10.1109/UBMK63289.2024.10773489https://hdl.handle.net/20.500.12573/3880This study investigates the effectiveness of various machine learning (ML) and deep learning (DL) techniques for large-scale sentiment analysis on Twitter data. We leverage a publicly available dataset of one million tweets, annotated with four sentiment labels (positive, negative, uncertainty, and liti-gious), to train and evaluate a range of models. Our experiments demonstrate that traditional ML algorithms, particularly XG-Boost, achieve high performance, with the best F1 score reaching 95.81% using a combination of unigrams and bigrams. Among DL models, a hybrid CNN-BiGRU architecture yields the highest average F1 score of 95.42%. Our findings highlight the strengths of different approaches for sentiment analysis on Twitter data and emphasize the importance of data preprocessing and model selection for achieving optimal performance. © 2025 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessDeep LearningMachine LearningSentiment AnalysisText MiningAdversarial Machine LearningContrastive LearningDeep Reinforcement LearningAnalysis ModelsDeep LearningF1 ScoresLarge-ScalesLearning TechniquesMachine-LearningPositive/NegativeSentiment AnalysisText-MiningUncertaintyTweetsConference Object10.1109/UBMK63289.2024.107734892-s2.0-85215516712