CLASSIFICATION AND SUMMARIZATION FOR INFORMATIVE TWEETS
P.BALASUBRAMANI
, Dr.S.RAJINI
HEADLINE TEXT, RANK SVM, NB, DATA
News Data is one of the structured as well as unstructured formatted data, which carries attributes like source, date, location, author, headline text and detailed data. To extract features from the textual data, now-a-days we prefer the Naive Bayes and Rank Support Vector Machine approach, which deals with all of the language and text related aspects. News articles are the basic structured as well as unstructured and text formatted data, which lead it to become the point of interest to the researchers. Various researches like classification of news articles according to domain depending on region or the area of interest of people using various machine learning techniques, sentiment analysis over the news articles using the emotional dictionary, text summarization on the news data so as to reduce the bulkiness of data, Special Character recognition using the NB and RANK SVM algorithms.
"CLASSIFICATION AND SUMMARIZATION FOR INFORMATIVE TWEETS ", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.a269-a275, February-2024, Available :https://tijer.org/TIJER/papers/TIJER2402033.pdf
Volume 11
Issue 2,
February-2024
Pages : a269-a275
Paper Reg. ID: TIJER_151213
Published Paper Id: TIJER2402033
Downloads: 000291
Research Area: Science and Technology
Country: VILLUPURAM, TAMILNADU, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
