• DocumentCode
    3588383
  • 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
  • fYear
    2014
  • Firstpage
    211
  • Lastpage
    216
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multi-Topic Conference (INMIC), 2014 IEEE 17th International
  • Print_ISBN
    978-1-4799-5754-5
  • Type

    conf

  • DOI
    10.1109/INMIC.2014.7097339
  • Filename
    7097339