Building Your Own Datasets for Machine Learning or (NLP) Purposes
Before you start your machine learning or Natural Language Processing project, you’ll need a dataset large enough to create a sample size that will give accurate results. While machine learning is traditionally used to identify current trends, we often assume the current behavior is similar to past behavior, although this is not always the case.
Before you start collecting data for your machine learning project, however, you’ll need to define the goal and find a sample dataset that is both large enough and good enough to develop a strong model for data analysis. Once the model is developed, you’ll need to decide how to measure the data.
But without that first step of access to datasets, many researchers, students and even enterprises would be stuck.
The Power of a Large Dataset
For example, say you want to develop a machine learning model that can predict stock movements. It can be quite difficult to develop a machine learning model for stocks that is accurate without a large dataset that has known outcomes. If you were able to test the model against historical data with stocks that you knew in advance would skyrocket or fall, you could test your model and predict with greater accuracy when certain events would occur.
Many leading organizations from around the world use Webhose’s datasets from our historical archive to build AI models for financial analysis. But we also have a range of free datasets that include blog posts, online message boards, news articles from different languages and categories, as well as negative and positive reviews of hotels, companies and movies.
Enhance Your Data Analysis with Rich Data Sets
Whether you’re a fintech company looking to gather historical data for predictive analytics and risk modeling, a researcher seeking training data for NLP, sentiment analysis or AI machine learning, Webhose’s free datasets can deliver insights and identify trends in a range of different industries. Webhose also offers the ability to create your own customized dataset from a historical database of over 100TB and multiple sources.
Organizations from all over the world have accessed our datasets to conduct market research for competitive intelligence, data-driven marketing and digital trends data.
Examples of these real-world applications include the content analysis of health news studies to determine the prevalence of nurses’ opinions in health news stories. Our data has been used to develop classification models built on reverse plagiarism and natural language processing for fake news detection. The models have successfully identified fake news more accurately than humans would otherwise.