Classification of financial news articles using machine learning algorithms
subject: Classifications; n-grams; Stock market; Support vector machine; Text mining
description: The opinion helps in determining the direction of the stock market. Information hidden in news articles is an information treasure which needs to be extracted. The present study is conducted to explore the application of text mining in binning the financial articles according to the opinion expressed inside them. It is discovered that using the tri-n-gram feature extraction process in conjugation with Support Vector machines increases the reliability and precision of the binning process. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.
creator: Saxena A.; Banik A.; Saswat C.; Joan Jose M.; Bhagat V.
source: Lecture Notes in Networks and Systems, Vol-132, pp. 193-199.
publisher: Springer
date: 2021-01-01
rights: Restricted Access
relation: ISSN: 23673370
format: Online
language: English
type: Book chapter
coverage: Saxena A., CHRIST (Deemed to be University), Bengaluru, India; Banik A., CHRIST (Deemed to be University), Bengaluru, India; Saswat C., CHRIST (Deemed to be University), Bengaluru, India; Joan Jose M., CHRIST (Deemed to be University), Bengaluru, India; Bhagat V., CHRIST (Deemed to be University), Bengaluru, India