SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model

Abstract

The system developed by the SSN MLRG1 team for Semeval-2017 task 5 on fine-grained sentiment analysis uses Multiple Kernel Gaussian Process for identifying the optimistic and pessimistic sentiments associated with companies and stocks. Since the comments on the same companies and stocks may display different emotions depending on time, their properities like smoothness and periodicity may vary. Our experiments show that while single Kernel Gaussian Process can learn some properties well, Multiple Kernel Gaussian Process are effective in learning the presence of different properties.

Dynamics Computational Sentiment Analysis in Financial Markets

“…Cosine Similarity Jiang et al [26] 0.778 Ghosal et al [27] 0.751 Deborah et al [63] 0 Our analysis reveals that all BERT-based models surpass the SemEval-2017 top performers, demonstrating the evolving effectiveness of BERT-based architectures in financial text sentiment analysis. Notably, RoBERTa, upon which our SDA is based, shows superior results due to its optimized training strategy and the absence of a next-sentence prediction objective, enhancing its relevance for tasks focused purely on sentiment analysis [61].…”