Contenido del Curso
Introduction to RNNs
Introduction to RNNs
Loading and Preprocessing the Data
In this chapter, we focus on the important task of data cleaning and preprocessing for sentiment analysis. We use the IMDB dataset for movie reviews, which contains labeled text data. Preprocessing the text data is a crucial step in preparing it for analysis and building an effective model. This chapter covers the cleaning process, including 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.
¡Gracias por tus comentarios!