Analyzing the Trade-offs between Runtime and Accuracy in Classification Algorithms for Natural Language Processing

Abstract

This research aims to analyze the trade-offs between runtime and accuracy in classification algorithms for Natural Language Processing (NLP) and propose an optimization framework for balancing these trade-offs. The study employs a quantitative approach and evaluates the performance of different classification algorithms using metrics such as precision, recall, F1-score, and AUC. The population for this study is all publicly available datasets for NLP classification, and the data is collected using open-source NLP tools. The study shows that certain classification algorithms such as Random Forest, Decision Trees, Naive Bayes, SVM, or Neural Networks perform better than others in terms of both runtime and accuracy. However, some algorithms are faster but less accurate, while others are slower but more accurate. The analysis provided insights into how the choice of algorithm affects the trade-offs between runtime and accuracy in NLP. Based on the results, an optimization framework is proposed that can assist researchers and practitioners in NLP to choose the optimal algorithm for a given task and dataset, considering the desired balance between runtime and accuracy. This research provides valuable insights into the trade-offs between runtime and accuracy in NLP classification algorithms and proposes a framework that can help researchers make informed decisions about which algorithm to choose.