Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Leer Loading and Preprocessing the Data | Sentiment Analysis
Introduction to RNNs
course content

Cursusinhoud

Introduction to RNNs

Introduction to RNNs

1. Introduction to RNNs
2. Advanced RNN Variants
3. Time Series Analysis
4. Sentiment Analysis

book
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.

question mark

What is the purpose of the clean_pipeline function in text preprocessing?

Select the correct answer

Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

Sectie 4. Hoofdstuk 3
Onze excuses dat er iets mis is gegaan. Wat is er gebeurd?
some-alt