Natural language processing is a vital chunk in the machine learning armour. However, it needs massive amounts of data and training for the model to work well. One of the significant issues with NLP is the lack of training datasets that can cover vast fields of interest within the domain.
If you are starting out in this vast field, you might find it challenging and practically redundant to create your datasets. Especially when there are quality NLP datasets available to train your machine learning models based on their purpose.
The NLP market is slated to grow at a CAGR of 11.7% during 2018 and 2026 to reach $28.6 Billion by 2026. Thanks to the growing demand for NLP and machine learning, it is now possible to get your hands on quality datasets catering to sentiment analysis, reviews, question and answers analysis, and speech analysis datasets.
The NLP Datasets For Machine Learning You Can Trust
Since countless datasets – focussing on various needs – are being released almost every day, it can be challenging to access quality, reliable, and best datasets. Here, we have made the work easier for you, as we have presented you with curated datasets segregated based on the categories they serve.
UCI’s Spambase (Link)
Spambase, created at the Hewlett-Packard Labs, has a collection of spam emails by the users, aiming to develop a personalized spam filter. It has more than 4600 observations from email messages, out of which close to 1820 are spam.
Enron dataset (Link)
The Enron dataset has a vast collection of anonymized ‘real’ emails available to the public to train their machine learning models. It boasts more than half a million emails from over 150 users, predominantly Enron’s senior management. This dataset is available for use in both structured and unstructured formats. To spruce up the unstructured data, you have to apply data processing techniques.
Recommender Systems dataset (Link)
The Recommender System dataset is a huge collection of various datasets containing different features such as,
- Product reviews
- Star ratings
- Fitness tracking
- Song data
- Social networks
- User/item interactions
- GPS data
Dictionaries for Movies and Finance (Link)
The Dictionaries for Movies and Finance dataset provides domain-specific dictionaries for positive or negative polarity in Finance fillings and movie reviews. These dictionaries are drawn from IMDb and U.S Form-8 fillings.
Sentiment 140 (Link)
Sentiment 140 has more than 160,000 tweets with various emoticons categorized in 6 different fields: tweet date, polarity, text, user name, ID, and query. This dataset makes it possible for you to discover the sentiment of a brand, a product, or even a topic based on Twitter activity. Since this dataset is automatically created, unlike other human-annotated tweets, it classifies tweets with positive emotions and negative emotions as unfavorable.
Multi-Domain Sentiment dataset (Link)
This Multi-domain sentiment dataset is a repository of Amazon reviews for various products. Some product categories, such as books, have reviews running into thousands, while others have only a few hundred reviews. Besides, the reviews with star ratings can be converted into binary labels.
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The Wiki QA Corpus (Link)
Created to help the open-domain question and answer research, the WiKi QA Corpus is one of the most extensive publicly available datasets. Compiled from the Bing search engine query logs, it comes with question-and-answer pairs. It has more than 3000 questions and 1500 labeled answer sentences.
Legal Case Reports Dataset (Link)
Legal Case Reports dataset has a collection of 4000 legal cases and can be used to train for automatic text summarization and citation analysis. Each document, catchphrases, citation classes, citation catchphrases, and more are used.
Jeopardy dataset is a collection of more than 200,000 questions featured in the popular quiz TV show brought together by a Reddit user. Each data point is classified by its aired date, episode number, value, round, and question/answer.
Spoken Wikipedia Corpora (Link)
This dataset is perfect for everyone looking to go beyond the English language. This dataset has a collection of articles spoken in Dutch and German and English. It has a diverse range of topics and speaker sets running into hundreds of hours.
2000 HUB5 English (Link)
The 2000 HUB5 English dataset has 40 telephone conversation transcripts in the English language. The data is provided by the National Institute of Standards and Technology, and its main focus is on recognizing conversational speech and converting speech into text.
LibriSpeech dataset is a collection of almost 1000 hours of English speech taken and properly segmented by topics into chapters from audio books, making it a perfect tool for Natural Language Processing.
Yelp Reviews (Link)
The Yelp dataset has a vast collection of about 8.5 million reviews of 160,000 plus businesses, their reviews, and user data. The reviews can be used to train your models on sentiment analysis. Besides, this dataset also has more than 200,000 pictures covering eight metropolitan locations.
IMDB Reviews (Link)
IMDB reviews are among the most popular datasets containing cast information, ratings, description, and genre for more than 50 thousand movies. This dataset can be used to test and train your machine learning models.
Amazon Reviews and Ratings Dataset (Link)
Amazon review and rating dataset contain a valuable collection of metadata and reviews of different products from Amazon collected from 1996 to 2014 – about 142.8 million records. The metadata includes the price, product description, brand, category, and more, while the reviews have text quality, the text’s usefulness, ratings, and more.
So, which dataset have you chosen to train your machine learning model on?
As we go, we will leave you with a pro-tip.
Make sure to thoroughly go through the README file before picking an NLP dataset for your needs. The dataset will contain all the necessary information you might require, such as the dataset’s content, the various parameters on which the data has been categorized, and the probable use cases of the dataset.
Regardless of the models you build, there is an exciting prospect of integrating our machines more closely and intrinsically with our lives. With NLP, the possibilities for business, movies, speech recognition, finance, and more are increased manifold. If you are looking for more such datasets Click Here.