A concept that aims to understand the mechanism behind programming computers to process and analyze large amounts of natural language data, tends to yield a massive field of research. Exploring such a diverse area tends to not only be a challenging situation for those navigating through it but also sometimes it’s difficult to even know how to take the first step into the search for dataSo, In hopes of easing you into the world of natural language processing, we’ve combined a list of online Datasets for Natural Language Processing-NLP that cover a wide arrange of topics. 

Discover Applications of NLP- Natural Language Processing

NLP datasets

Let’s see a list of NLP Datasets that can help you decipher the plethora of information available online.

Datasets for Sentiment Analysis

Refer to the use of natural language processing to identify, extract, quantify & study affective states & subjective information; all of which point to the need for a large, specialized dataset. So, what are some of the datasets that can help you do that?

Stanford Sentiment Treebank home to over 10,000 clips from Rotten Tomatoes, Stanford’s dataset is to help identify sentiment in longer phrases i.e. get the system accustomed to detailed data.

Multidomain Sentiment Analysis Dataset – despite being an older dataset, it offers many product reviews taken from Amazon that help with the provision of diverse data.

IMDB Reviews like Stanford’s treebank, this dataset consists of over 25,000 movie reviews that are useful for a rather binary classification use.


Text Datasets

Not only are these datasets easier to access, but they are also easier to input and use for natural language processing tasks about the inclusion of chatbots and voice recognition.
The Blog Authorship Corpus – with over 681,000 posts by over 19,000 independent bloggers, this dataset is home to over 140 million words; which on its own poses it as a valuable dataset.

UCI’s Spambase a creation of the team at Hewlett-Packard, this dataset consists of a wide array of spam email that can be in use to create spam filters.

The WikiQA Corpusone of the most accessible collections of questions/answers, this dataset is here for research purposes in the domain of question answering but has now become a public depository for anyone concerned with natural language processing.
Wordnet a product of researchers at Princeton University, Wordnet offers a large database consisting of synonyms in the English language with each describing a unique concept.
Yelp Reviews available for public, this dataset contains millions of reviews received by Yelp over the years

Audio Datasets

When training natural language processing applications to act as virtual assistants, navigation, or any other sound-activated systems, one finds audio speech datasets as most useful.
LibriSpeech –with over 1,000 hours of English speech available from a range of audiobooks dictated by many speakers, this dataset stands out as one of the most diverse resources available.
Free Spoken Digit Dataset – since numbers are key, this collection offers 1,500 recordings of digits spoken in the English language

General Datasets

In a world where knowledge knows no bounds, there’s a great chance that you might be looking for something that doesn’t fall under any of the above-mentioned categories. So, for some general datasets that can help you with any natural language processing task, read on!
Wikipedia Links Data – if there’s any dataset that has the potential to hold universal information, it’s the dataset by Google consisting of webpages directed to Wikipedia
Jeopardy – whether you’re a fan of the show, you will be a fan of this dataset; with over 200,000 questions and answers from the show, this dataset is home to a range of diverse information
SMS Spam Collection in English – who says spam texts can’t be useful? With over 5,000 spam messages, this dataset offers information like none other

Summing Up…

The above information is a suite of standard datasets that are in use for natural language processing tasks when getting started with deep learning.
It is preferable to use small datasets and do not take too long to fit models. Also, it is very helpful to use standard datasets that are understandable and you can compare the outcomes to have a clear picture of the progress that you are making.
So with this information, find your datasets and go on!