Effect of Stemming on Hindi Text Classification

Abstract.  Text classification is very useful to search large amount of textual data available online by dividing it into smaller relevant units. Now a day’s large amount of digital documents are available in Indian languages. Designing text classifiers in Indian languages is one of the research areas so that people can search and read required documents in their local languages. In proposed work tried to design Text classifier for Hindi text documents and tried to show how stemmer affects the performance of Hindi text classifiers. Stemming is a process to convert words in any language to its base or root words. Stemmers are used for written documents not for spoken languages. Performance of many applications such as text summarization, Information Retrieval (IR) system,text classification systems, syntactic parsing can be improved by applying stemmers. Stemmer eliminates suffix or prefix of the word and form original root word. These root words helps in the preprocessing step required in many algorithms. We applied various stemmers on Hindi text classification models. Experiments and results show that performance of the classifiers is improved by applying stemmers.