Abstract
The paper highlights the significance of efficient text preprocessing strategies in Natural Language Processing (NLP), a field focused on enabling machines to understand and interpret human language. Text preprocessing is a crucial step in converting unstructured text into a machine-understandable format. It plays a vital role in various text classification tasks, including web search, document classification, chatbots, and virtual assistants. Techniques such as tokenization, stop word removal, and lemmatization are carefully studied and applied in a specific order to ensure accurate and efficient information retrieval. The paper emphasizes the importance of selecting and ordering preprocessing techniques wisely to achieve high-quality results. Effective text preprocessing involves cleaning and filtering textual data to eliminate noise and enhance efficiency. Furthermore, it provides insights into the impact of different techniques, such as raw text, tokenization, stop word removal, and stemming, using a Python implementation.
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