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346494

Sentiment Analysis of English Newspapers: A Corpus-based study

Article

Last updated: 24 Dec 2024

Subjects

-

Tags

بحوث اللغات والآداب

Abstract

Abstract
The theoretical and methodological approaches deployed in analyzing the data were corpus linguistics (CL) and sentiment analysis approach as a way of analyzing newspapers media discourse and sentiment representations of Saudi Arabia. The use of technology in extracting data for this study was a must due the huge amount of corpora texts which were investigated in the current research. The main source of data collection and analysis was done through sketch engine (SE). Also, the R programming language was used during the analysis process which provides several libraries for coding that makes it easier to conduct this type of research including: Tidyverse, Tidytext, Syuzhet, Textstem, Ggplot2, Readxl and Writexl. The newspapers corpora were extracted from SE dataset between the period of 1993-2013. Finally, the findings were classified in two ways: one was for each newspaper for the entire time period, and the second was for the entire newspapers' corpora data together.

DOI

10.21608/jfabsu.2023.244029.1287

Keywords

Sentiment Analysis, Corpus linguistics, CDA, media discourse, Machine Learning

Authors

First Name

Dr. Muhammad

Last Name

Alrayes

MiddleName

-

Affiliation

كلية اللغات وعلومها، جامعة الملك سعود

Email

alrayees@gmail.com

City

-

Orcid

-

Volume

8

Article Issue

70

Related Issue

46434

Issue Date

2024-03-01

Receive Date

2023-10-22

Publish Date

2024-03-01

Page Start

39

Page End

58

Print ISSN

2090-9012

Online ISSN

2090-9829

Link

https://jfabsu.journals.ekb.eg/article_346494.html

Detail API

https://jfabsu.journals.ekb.eg/service?article_code=346494

Order

13

Type

المقالة الأصلية

Type Code

1,009

Publication Type

Journal

Publication Title

مجلة کلية الآداب . جامعة بني سويف

Publication Link

https://jfabsu.journals.ekb.eg/

MainTitle

Sentiment Analysis of English Newspapers: A Corpus-based study

Details

Type

Article

Created At

24 Dec 2024