What is NLP?
NLP (Natural Language Processing) helps computers understand human language. It’s like teaching computers to read, understand, and respond to text and speech the way humans do.
What can NLP do?
- Turn messy text into organized data
- Understand if comments are positive or negative
- Translate between languages
- Create summaries of long texts
- And much more!
- Getting Started with NLP:
To build good NLP systems, you need lots of examples to train them – just like how humans learn better with more practice. The good news is that there are many free resources where you can find these examples: Hugging Face, Kaggle and GitHub
NLP Market Size and Growth:
As of 2023, the Natural Language Processing (NLP) market was valued at around $26 billion. It’s expected to grow significantly, with a compound annual growth rate (CAGR) of about 30% from 2023 to 2030. This growth is driven by increasing demand for NLP applications in industries like healthcare, finance, and customer service.
How to choose a good NLP dataset, consider the following factors:
- Relevance: Ensure the dataset aligns with your specific task or domain.
- Size: Larger datasets generally improve model performance, but balance size with quality.
- Diversity: Look for datasets with varied language styles and contexts to enhance model robustness.
- Quality: Check for well-labeled and accurate data to avoid introducing errors.
- Accessibility: Ensure the dataset is available for use and consider any licensing restrictions.
- Preprocessing: Determine if the dataset requires significant cleaning or preprocessing.
- Community Support: Popular datasets often have more resources and community support, which can be helpful.
By evaluating these factors, you can select a dataset that best suits your project’s needs
Top 33 Must-See Open Datasets for NLP
General
-
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
- Timestamps
- User/item interactions
- GPS data
-
Penn Treebank (Link)
This corpus, from the Wall Street Journal, is popular for testing sequence labeling models.
-
NLTK (Link)
This Python library provides access to over 100 corpora and lexical resources for NLP. It also includes the NLTK book, a training course for using the library.
-
Universal Dependencies (Link)
UD provides a consistent way to annotate grammar, with resources in over 100 languages, 200 treebanks, and support from over 300 community members.
Sentiment Analysis
-
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.
-
Standford Sentiment TreeBank (Link)
This NLP dataset from Rotten Tomatoes includes longer phrases and more detailed text examples.
-
The Blog Authorship Corpus (Link)
This collection has blog posts with nearly 1.4 million words, each blog is a separate dataset.
-
OpinRank Dataset (Link)
300,000 reviews from Edmunds and TripAdvisor, organized by car model or travel destination and hotel.