EconPapers    
Economics at your fingertips  
 

A framework for text mining on Twitter: a case study on joint comprehensive plan of action (JCPOA)- between 2015 and 2019

Rashid Behzadidoost (), Mahdieh Hasheminezhad (), Mohammad Farshi (), Vali Derhami () and Farinaz Alamiyan-Harandi ()
Additional contact information
Rashid Behzadidoost: Yazd University
Mahdieh Hasheminezhad: Yazd University
Mohammad Farshi: Yazd University
Vali Derhami: Yazd University
Farinaz Alamiyan-Harandi: Yazd University

Quality & Quantity: International Journal of Methodology, 2022, vol. 56, issue 5, No 9, 3053-3084

Abstract: Abstract In the big data era, there is a necessity for effective frameworks to collect, retrieve, and manage data. As not all tweets are hashtagged by users, retrieving them is a complicated task. To address this issue, we present a rule-based expert system classifier that uses the well-known concept of fingerprint in the judicial sciences. This expert system using defined rules first takes a fingerprint from the tweets of an emerging topic. After that, for being robust the fingerprint, using a rule-based search, the fingerprint with its neighbor features is to be updated. For detecting the unhashtagged tweets of the topic, each tweet in question checks itself with the generated fingerprint. By using the Twitter APIs of Streaming API and REST API, there is no way to access old Twitter data. To address this issue, we present a hybrid approach of Web scraping and Twitter streaming API. When the presented framework is compared to other similar works, there are (1) a novel two-class classification using an expert system approach that can intelligently and robustly detect the most of tweets of the emerging topics although they do not have the hashtag of the topic.; (2) a practical method for extracting old Twitter data. Also, we made a comparative text mining in 195649 collected Persian and English tweets about JCPOA. The JCPOA is one of the most important international treaties about the nuclear program between the Islamic Republic of Iran and the USA, China, France, Russia, Germany, and England.

Keywords: Text mining; Topic detection; Sentiment analysis; Fingerprint; Twitter; JCPOA; Iran deal (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11135-021-01239-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:56:y:2022:i:5:d:10.1007_s11135-021-01239-y

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-021-01239-y

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:qualqt:v:56:y:2022:i:5:d:10.1007_s11135-021-01239-y