Detecting Types of News Using Hierarchical Machine Learning Model with Text Classification
Artificial Intelligence, Machine Learning, Text Analytics, Classification, Feature Extraction, Hierarchical Model, Model Overfitting
News is one of the important aspects of human life from which they can gather the required information. In the early time, the news have been gathered by readers from the newspaper or from the news channels. With the advancement of technology, Social Media comes into the scenario from where readers can get their required news and many more things. In all cases, News can be of different types which are somehow difficult for the reader to identify. Machine Learning plays an important role here to detect the type of news with the implication of Natural Language Processing to analyze the text and to identify the types. In this research, the type of news has been detected by collecting the Insorts news database from Kaggle. The news texts have been prepared by cleaning and vectorizing with the implication of Term Frequency Inverse Document Frequency and Count Vectorization and the models of machine learning have been applied. In this context, the Hierarchical Machine Learning model has been proposed that combines the selected state-of-the-art models through Stacking Classifiers and Voting Classifiers. With the application of all state-of-the-art models and the proposed model, it has been observed that the proposed model has detected the type of news with the highest accuracy (94.22% using TFIDF and Unigram) which is also seen to be higher compared to the existing approaches
"Detecting Types of News Using Hierarchical Machine Learning Model with Text Classification", IJEDR - INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH (www.IJEDR.org), ISSN:2321-9939, Vol.10, Issue 4, page no.20-31, November 2022, Available :https://rjwave.org/IJEDR/papers/IJEDR2204003.pdf
Volume 10
Issue 4,
October-2022
Pages : 20-31
Paper Reg. ID: IJEDR_220215
Published Paper Id: IJEDR2204003
Research Area: Engineering
Country: Kolkata, West Bengal, India
DOI: http://doi.one/10.1729/Journal.31970
ISSN: 2321-9939 | IMPACT FACTOR: 9.37 Calculated By Google Scholar | ESTD YEAR: 2013
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.37 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJEDR (IJ Publication) Janvi Wave
