Creating a Wordcloud using NLP and TF-IDF in Python

von
Coursera Project Network
In diesem angeleitetes Projekt werden Sie:

Learn how to clean a dataset by removing encodings and unwanted words/characters

Learn how to lemmatize a text and fit a TF-IDF model

Learn how to create a wordcloud using TF-IDF scores

Clock1.5 hours
BeginnerAnfänger
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

By the end of this project, you will learn how to create a professional looking wordcloud from a text dataset in Python. You will use an open source dataset containing Christmas recipes and will create a wordcloud of the most important ingredients used in these recipes. I will teach you how load a JSON dataset, clean the dataset by removing encodings and unwanted characters, and lemmatize your dataset. I will also teach you how to calculate TF-IDF weights of words in your dataset and use these weights to create a wordcloud. You will create a ready-to-use Jupyter notebook for creating a wordcloud on any text dataset. Lemmatization is a process of removing inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma. TF-IDF stands for term frequency-inverse document frequency. TF-IDF gives a weight to each word which tells how important that term is. Using both lemmatization and TF-IDF, one can find the important words in the text dataset and use these important words to create the wordcloud. For example, these datasets could be customer complaints and the business can focus on the important issues that the customers are facing. Wordcloud is a powerful resource which can be used in reports and presentations. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Kompetenzen, die Sie erwerben werden

  • Natural Language Toolkit (NLTK)
  • Python Programming
  • Term Frequency Inverse Document Frequency (TF-IDF)
  • Wordnet

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  1. Load a JSON dataset in Python

  2. Clean the dataset

  3. Remove encodings

  4. Lemmatize the text

  5. Fit TF-IDF model

  6. Create a Wordcloud

Ablauf angeleiteter Projekte

Ihr Arbeitsbereich ist ein Cloud-Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

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