The focus is on the important task of data cleaning and preprocessing for sentiment analysis using the IMDB dataset of labeled movie reviews. Preprocessing is a crucial step for preparing text data for analysis and building an effective model. The cleaning process includes removing unwanted characters, correcting spelling, tokenizing, and lemmatizing the text.

Text cleaning:

The first step in text preprocessing is to clean the raw text by removing unnecessary elements such as links, punctuation, HTML tags, numbers, emojis, and non-ASCII characters. the following cleaning functions are applied:

• Removing links: URLs are removed using the rm_link function, which matches and removes HTTP or HTTPS URLs;
• Handling punctuation: the rm_punct2 function removes unwanted punctuation marks;
• Removing HTML tags: the rm_html function eliminates any HTML tags from the text;
• Spacing between punctuation: the space_bt_punct function adds spaces between punctuation marks and removes extra spaces;
• Removing numbers: the rm_number function eliminates any numeric characters;
• Whitespace handling: the rm_whitespaces function removes extra spaces between words;
• Non-ASCII characters: the rm_nonascii function removes any characters that are not ASCII;
• Removing emojis: the rm_emoji function removes emojis from the text;
• Spell correction: the spell_correction function corrects repeated letters in words, such as "looooove" to "love".

In summary, data cleaning and preprocessing are crucial steps in the sentiment analysis pipeline. By removing noise and standardizing the text, we make it easier for machine learning models to focus on the relevant features for tasks like sentiment classification.