<div>Textual emotion classification is a task in affective AI that branches from sentiment analysis and focuses on identifying emotions expressed in a given text excerpt. </div><div>It has a wide variety of applications that improve human-computer interactions, particularly to empower computers to understand subjective human language better. </div><div>Significant research has been done on this task, but very little of that research leverages one of the most emotion-bearing symbols we have used in modern communication: Emojis.</div><div>In this thesis, we propose several transformer-based models for emotion classification that processes emojis as input tokens and leverages pretrained models and uses them</div><div>, a model that processes Emojis as textual inputs and leverages DeepMoji to generate affective feature vectors used as reference when aggregating different modalities of text encoding. </div><div>To evaluate ReferEmo, we experimented on the SemEval 2018 and GoEmotions datasets, two benchmark datasets for emotion classification, and achieved competitive performance compared to state-of-the-art models tested on these datasets. Notably, our model performs better on the underrepresented classes of each dataset.</div>

As the cryptocurrency markets continuously grow, so does the need for reliable analytical tools for price prediction. This study conducted a comparative analysis of machine learning (ML) algorithms for cryptocurrency price prediction. Through a literature review, three common and reliable ML algorithms for cryptocurrency price prediction were identified: Long Short-Term Memory (LSTM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Utilizing the Bitcoin All Time History dataset from TradingView, the study assessed both the individual performance of each algorithm and the potential of ensemble methods to enhance predictive accuracy. The results reveal that the LSTM algorithm outperformed RF and XGBoost in terms of predictive accuracy according to the metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Additionally, two ensemble approaches were tested: Ensemble 1, which enhanced the LSTM model with the combined predictions from RF and XGBoost, and Ensemble 2, which integrated predictions from all three models. Ensemble 2 demonstrated the highest predictive performance among all models, highlighting the advantages of using ensemble approaches for more robust predictions.