Title :
News classification based on their headlines: A review
Author :
Rana, Mazhar Iqbal ; Khalid, Shehzad ; Akbar, Muhammad Usman
Author_Institution :
Dept. of Comput. Eng., Bahria Univ. Islamabad, Islamabad, Pakistan
Abstract :
For the last few years, text mining has been gaining significant importance. Since Knowledge is now available to users through variety of sources i.e. electronic media, digital media, print media, and many more. Due to huge availability of text in numerous forms, a lot of unstructured data has been recorded by research experts and have found numerous ways in literature to convert this scattered text into defined structured volume, commonly known as text classification. Focus on full text classification i.e. full news, huge documents, long length texts etc. is more prominent as compared to the short length text. In this paper, we have discussed text classification process, classifiers, and numerous feature extraction methodologies but all in context of short texts i.e. news classification based on their headlines. Existing classifiers and their working methodologies are being compared and results are presented effectively.
Keywords :
electronic publishing; feature extraction; pattern classification; text analysis; classifiers; feature extraction methodologies; headlines; news classification; short texts; text classification process; Accuracy; Classification algorithms; Decision trees; Indexing; Support vector machines; Text categorization; Text mining; Classification model; Machine Learning; News classification; News headlines classification; Text Mining; Text classifiers;
Conference_Titel :
Multi-Topic Conference (INMIC), 2014 IEEE 17th International
Print_ISBN :
978-1-4799-5754-5
DOI :
10.1109/INMIC.2014.7097339
