What is textual dataset?

Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response.

How do you create a dataset for text classification?

Sign up for free and build your own classifier following these four simple steps:

1. Create a new text classifier: Go to the dashboard, then click Create a Model, and choose Classifier:
2. Upload training data:
3. Define the tags for your model:
4. Tag data to train the classifier:

Where can I find NLP datasets?

In this article, we list down 10 free and open-source NLP datasets to kickstart your first NLP project.

• 1| The Blog Authorship Corpus.
• 2| Amazon Product Dataset.
• 4| LibriSpeech.
• 5| Free Spoken Digit Dataset (FSDD)
• 6| Stanford Question Answering Dataset (SQuAD)
• 8| Yelp Reviews.
• 9| WordNet.
• 10| TIMIT.

What does text data mean?

In this article, we will look at how to work with text data, which is definitely one of the most abundant sources of unstructured data. Text data usually consists of documents which can represent words, sentences or even paragraphs of free flowing text.

What is text mining used for?

Widely used in knowledge-driven organizations, text mining is the process of examining large collections of documents to discover new information or help answer specific research questions. Text mining identifies facts, relationships and assertions that would otherwise remain buried in the mass of textual big data.

Where do I find Reddit data?

Where and how can I access my Reddit data and information?