Handling Special Characters

Introduction

Special characters in text data refer to non-alphanumeric and non-whitespace characters, such as punctuation marks (!, @, #, $, %) and symbols (∞, ©, π) that go beyond standard letters and numbers. These characters can significantly impact text analysis and NLP tasks. For instance, special characters can affect how words are split during tokenization, potentially leading to incorrect interpretations and degraded performance in downstream tasks like sentiment analysis or machine translation, i.e., the special character “&” could pose difficulties if not appropriately managed during tokenization, given that it’s frequently used to denote brand names or collaborations such as AT&T and Johnson & Johnson. Mishandling it during text preprocessing would result in an erroneous dataset.