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Stopwords carry little semantic information and are often removed from textdata to reduce dataset size and improve machine learning model performance.Consequently, researchers have sought to develop techniques for generatingeffective stopword sets. Previous approaches have ranged from qualitativetechniques relying upon linguistic experts, to statistical approaches thatextract word importance using correlations or frequency-dependent metricscomputed on a corpus. We present a novel quantitative approach that employsiterative and recursive feature deletion algorithms to see which words can bedeleted from a pre-trained transformer's vocabulary with the least degradationto its performance, specifically for the task of sentiment analysis.Empirically, stopword lists generated via this approach drastically reducedataset size while negligibly impacting model performance, in one such exampleshrinking the corpus by 28.4% while improving the accuracy of a trainedlogistic regression model by 0.25%. In another instance, the corpus was shrunkby 63.7% with a 2.8% decrease in accuracy. These promising results indicatethat our approach can generate highly effective stopword sets for specific NLPtasks.

Quantitative Stopword Generation for Sentiment Analysis via Recursive and Iterative Deletion