With the rapid growth of social media and frequent anonymous interactions, hate speech has become widespread. As users express diverse opinions in digital spaces, the need for effective detection remains crucial. To address this, we propose a framework applicable to diverse hate speech types, combining sentence-level semantic representation vectors from the pre-trained Bidirectional Encoder Representations from Transformers (BERT) with sentiment score vectors from the Linguistic Inquiry and Word Count (LIWC) dictionary and the Valence Aware Dictionary for sEntiment Reasoning (VADER). This semantic-sentiment fusion integrates three deep learning models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Deep Neural Network (DNN) to enhance detection effectiveness. To verify generalizability, we used four datasets: two binary hate speech detection tasks, two multi-class tasks, and validation on another domain dataset. Results show that the proposed framework achieved the best performance, with accuracy up to 91.34%. This approach provides valuable direction for future research.

Predicting player performance in sports is a critical challenge with significant implications for team success, fan engagement, and financial outcomes. Although, in Major League Baseball (MLB), statistical methodologies such as sabermetrics have been widely used, the dynamic nature of sports makes accurate performance prediction a difficult task. Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions. This study addresses this challenge by employing the temporal fusion transformer (TFT), an advanced and cutting-edge deep learning model for complex data, to predict pitchers’ earned run average (ERA), a key metric in baseball performance analysis. The performance of the TFT model is evaluated against recurrent neural network-based approaches and existing projection systems. In experimental results, the TFT based model consistently outperformed its counterparts, demonstrating superior accuracy in pitcher performance prediction. By leveraging the advanced capabilities of TFT, this study contributes to more precise player evaluations and improves strategic planning in baseball.