Optimization of Sentiment Analysis Models Using Bayesian Hyperparameter Tuning

Analysis of Sentiments is the indispensable grind-stone in NLP (Natural Language Processing), seeks to excerpt idiosyncratic cue from textual data. The cue of sentiment anal-ysis models is fundamentally influenced by the vigilant tuning of their hyperparameters. This paper ventilates a Bayesian optimization framework to ameliorate the hyperparameters of different Machine Learning Algorithms for decked sentiment analysis performance. By employing Bayesian probability theory, this advent expertly reconnoitres the hyperparameter space, illuminating optimal configurations that meliorates model uni-versality and prediction accuracy. The proposed framework is calculated on a benchmark dataset, demonstrating substantial amendments in accuracy compared to traditional grid search and random search tactics. The findings features the probable of Bayesian optimization in optimizing sentiment analysis models, macadamizing the way for more authentic and steady sentiment analysis applications.