GCRIS

In today’s world, high volume of text is produced and stored continuously by the help of computer systems and Internet. And again by the help of Internet, those huge amount of text data is accessible to everyone. But when considering the size of the produced text, it is really hard for people to analyze the huge amounts of text data and discover the meaningful information in that data. Machine learning techniques and computer power emerges at this point, in order to analyze data and discover meaningful information to help people to access the summarized information. First step to analyze text data is to represent data in a numerical format, as machine learning techniques can only use numerical inputs. There are several methods for data representation; such as TF-IDF (Term Frequency - Inverse Document Frequency), Bag of Words, Word2Vec and Doc2Vec. Second step to analyze text data is to use machine learning algorithms by using the numerical representation of text data as input. There are supervised and unsupervised machine learning techniques to be decided to be used according to the structure of the problem and the data. In this study, news documents published in some publications in United States, such as New York Times, Reuters and Washington Post will be clustered into topics in order to categorize them and ease the investigation of them. Three types of data representation methods will be examined in detail and will be used, which are Bag of Words, TF-IDF and Doc2Vec representations. And finally, as the news data is an unlabeled set of documents, K-Means clustering algorithm will be used which is an unsupervised learning technique, by using both Euclidean Distance and Cosine Similarity metrics. Categorization will be performed multiple times with different category counts, meaning with different K values, and most meaningful category count will be determined after examining the clustering results.

Yüksek Lisans Tezleri

Browse

Filters

Settings

Sort By

Results per page

Search Results