Text Classification can be useful in understanding customer behaviour by categorizing conversations on social networks, feedback and other web sources. Search engines, newspapers, or e-commerce portals categorize their content or products to facilitate the search and navigation
Text Classification is a very important tool for categorisation of data sets on a large scale. Text Classification assigns one or more categories to a text, allowing to structure data for better insights. We utilize a comprehensive list of taxonomy to categorize the text content or web page contents into definitive tags.
Text Classification might involve different types of datasets and we have solutions for all the variations. A client might have datasets that are:
1. Untagged datasets, to be classified into generic categories. We have our API for classifying sentences/articles into standard IAB categories.
2. Tagged Datasets with multiple categories. This type of classifier can classify text into 10s and 100s of user defined categories like E-commerce product categories.
3. Partially tagged datasets, where only some of the data is tagged with categories and a lot of untagged data is available. Such classifiers are used to do intent analysis.
4. Chat Lingo and shorthand text classification. For such classification, where words are shortened and are not grammatically accurate, we use character level embeddings and hence can get around users' tendency to shorthand text.
Text classification can come in handy whenever there is a need of organizing large corpus. Platforms such as E-commerce, news agencies, content curators, blogs, directories, government, academia, and likes can use automated technologies to classify and tag content and products. Text classification can be customized heavily to aid highly selective situations involving analyzing and classifying texts. Read more application of text classification here.
Highly accurate classification of unstructured textual data
State of the art technology to provide accurate results real-time
Can be trained on custom dataset to obtain similar accuracy and performance