  Stopwords are commonly used words in a language that are often considered to
be of little value in determining the meaning or significance of a document.
These words occur frequently in most texts and don't provide much useful
information for tasks like sentiment analysis and text classification. English,
which is a high-resource language, takes advantage of the availability of
stopwords, whereas low-resource Indian languages like Marathi are very limited,
standardized, and can be used in available packages, but the number of
available words in those packages is low. Our work targets the curation of
stopwords in the Marathi language using the MahaCorpus, with 24.8 million
sentences. We make use of the TF-IDF approach coupled with human evaluation to
curate a strong stopword list of 400 words. We apply the stop word removal to
the text classification task and show its efficacy. The work also presents a
simple recipe for stopword curation in a low-resource language. The stopwords
are integrated into the mahaNLP library and publicly available on
https://github.com/l3cube-pune/MarathiNLP .
