Since the so-called Web 2.0 has arrived, user-generated content has become an expected behavior on the Web. Commenting, rating and other types of contributions are widespread. While users are able to provide content (unlike in Web 1.0), they still can not introduce user-generated behavior. In other words, their contributions are limited to the designs of the application builders.
This is a project which evolves Turkish text classification of two different classes (Financial texts or Random Wikipedia texts) and their prediction results. As a classifier, Logistic regression is used. The intention of this project is to successfully identify the texts which contain financial news or data about companies from the other texts which are in random topics. The importance of the financial news documents can be understood if we look at the correlation between the stock price of a company, and the publicly published news about this company.
With the widespread of Internet and social media, the information flow growth unboundedly and without control . So there are much more disinformation today than a copule years ago . One of the most common widespread wrong information type is poems that actually do not belong to the author specified in the social media post or anywhere else on the Internet. Our application uses machine learning to detect author of the Turkish poems.
