Performance evaluation of text-mining models with Hindi stopwords lists

Abstract

Nowadays, several news portals, government websites, and social media sites are generating a massive amount of digitalized Hindi textual information. Stopword removal is a significant factor in text mining tasks that helps the miner to enhance the performance of a system. This paper attempts to construct the corpus specific stopwords lists for Hindi text documents using statistical and knowledge-based methods. In order to prepare the stopwords list, the proposed method considers the ranking of the words given by different methods followed by normalization of the outcomes of these methods using the social choice theory based vote ranking method. Further, we propose an evaluation method to evaluate the prepared stopword lists and investigate their behavior using text mining models. We also compare our prepared stopword lists with the baselines and conclude that the technique which fetches the best features does not necessarily identify the candidate stop words. To the best of our knowledge, the proposed approach guarantees the removal of candidate stop words and has the least information dissipation.

Keywords

• Stopwords
• Hindi language
• Text classification
• Text clustering
• Topic modeling