🔎📝Datasets for Natural Language Processing
Sentiment analysis - a set of different datasets, each of which contains the necessary information for analyzing the sentiment of a text. So, the data taken from IMDb is a binary set for sentiment analysis. It consists of 50,000 reviews from the Movie Database (IMDb), marked as either positive or negative.
WikiQA is a collection of question and suggestion pairs. They were collected and annotated to investigate responses to questions in open domains. WikiQA is created using a more natural process. It includes questions for which there are no correct sentences, allowing researchers to work on the response trigger, a critical component of any QA system.
Amazon Reviews dataset - This dataset consists of several million Amazon customer reviews and their ratings. The dataset is used to enable fastText to learn by analyzing consumer sentiment. The idea is that despite the huge amount of data, this is a real business challenge. The model is trained in minutes. This is what sets Amazon Reviews apart from its peers.
Yelp dataset - The Yelp dataset is a collection of businesses, testimonials, and user data that can be applied to a Pet project and academia. You can also use Yelp to teach students how to work with databases, when learning NLP, and as a sample of production data. The dataset is available as JSON files and is a "classic" in natural language processing.
Text classification - Text classification is the task of assigning an appropriate category to a sentence or document. The categories depend on the selected dataset and may vary depending on the topics. For example, TREC is a question classification dataset that consists of fact-based open-ended questions. They are divided into broad semantic categories. The dataset has six-grade (TREC-6) and fifty-grade (TREC-50) versions. Both versions include 5452 training and 500 test cases.