IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v56y2022i5d10.1007_s11135-021-01239-y.html
   My bibliography  Save this article

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

Author

Listed:
  • Rashid Behzadidoost

    (Yazd University
    Yazd University)

  • Mahdieh Hasheminezhad

    (Yazd University
    Yazd University)

  • Mohammad Farshi

    (Yazd University
    Yazd University)

  • Vali Derhami

    (Yazd University)

  • Farinaz Alamiyan-Harandi

    (Yazd University)

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.

Suggested Citation

  • Rashid Behzadidoost & Mahdieh Hasheminezhad & Mohammad Farshi & Vali Derhami & Farinaz Alamiyan-Harandi, 2022. "A framework for text mining on Twitter: a case study on joint comprehensive plan of action (JCPOA)- between 2015 and 2019," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3053-3084, October.
    • Handle: RePEc:spr:qualqt:v:56:y:2022:i:5:d:10.1007_s11135-021-01239-y
    DOI: 10.1007/s11135-021-01239-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-021-01239-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-021-01239-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xu, Shuo & Hao, Liyuan & An, Xin & Yang, Guancan & Wang, Feifei, 2019. "Emerging research topics detection with multiple machine learning models," Journal of Informetrics, Elsevier, vol. 13(4).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Javier Jiménez-Cabas & Lizeth Torres & Jorge de J. Lozoya-Santos, 2023. "Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks," Sustainability, MDPI, vol. 15(6), pages 1-16, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    2. Minchul Lee & Min Song, 2020. "Incorporating citation impact into analysis of research trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1191-1224, August.
    3. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    4. Ebadi, Ashkan & Auger, Alain & Gauthier, Yvan, 2022. "Detecting emerging technologies and their evolution using deep learning and weak signal analysis," Journal of Informetrics, Elsevier, vol. 16(4).
    5. Yang, Zaoli & Zhang, Weijian & Yuan, Fei & Islam, Nazrul, 2021. "Measuring topic network centrality for identifying technology and technological development in online communities," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    6. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    7. Ryosuke L. Ohniwa & Kunio Takeyasu & Aiko Hibino, 2022. "Researcher dynamics in the generation of emerging topics in life sciences and medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 871-884, February.
    8. Xin An & Xin Sun & Shuo Xu, 2022. "Important citations identification with semi-supervised classification model," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6533-6555, November.
    9. Nhu Khoa Nguyen & Thierry Delahaut & Emanuela Boros & Antoine Doucet & Gael Lejeune, 2023. "Contextualizing Emerging Trends in Financial News Articles," Papers 2301.11318, arXiv.org.
    10. Wenjie Wei & Hongxu Liu & Zhuanlan Sun, 2022. "Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4315-4333, August.
    11. Wooseok Jang & Yongtae Park & Hyeonju Seol, 2021. "Identifying emerging technologies using expert opinions on the future: A topic modeling and fuzzy clustering approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6505-6532, August.
    12. Shuo Xu & Ling Li & Xin An & Liyuan Hao & Guancan Yang, 2021. "An approach for detecting the commonality and specialty between scientific publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7445-7475, September.
    13. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Xu, Haiyun & Yang, Guancan, 2022. "A semantic main path analysis method to identify multiple developmental trajectories," Journal of Informetrics, Elsevier, vol. 16(2).
    14. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    15. Jiang, Man & Yang, Siluo & Gao, Qiang, 2024. "Multidimensional indicators to identify emerging technologies: Perspective of technological knowledge flow," Journal of Informetrics, Elsevier, vol. 18(1).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:56:y:2022:i:5:d:10.1007_s11135-021-01239-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.