Sentiment Analysis of Machine Learning Algorithms: A Transformer-Based Approach

Machine learning algorithms have become pervasive in diverse applications, revolutionizing various domains. However, the abundance of algorithms, each designed for specific purposes, poses a challenge for both novice users and experts in selecting the most suitable model. This research addresses this issue through a comprehensive analysis, leveraging Natural Language Processing (NLP) techniques and the powerful Transformers library. Researcher's comments from published papers were analyzed using the Transformers library, presenting a novel approach that maps algorithm scores based on adjectives. Results indicate that Neural Networks consistently outperform other algorithms, providing valuable insights for practitioners. Our research contributes to a systematic evaluation framework, aiding researchers in algorithm selection.