Author
Listed:
- R. Tamilkodi
(Godavari Global University)
- B. Sujatha
(Godavari Global University
Godavari Global University)
- N. Leelavathy
(Godavari Global University
Godavari Global University)
Abstract
In the era of rapid internet expansion, social networking platforms have become indispensable channels for individuals to convey their emotions and opinions to a global audience. People employ various media types, including text, images, audio, and video, to articulate their sentiments. However, the sheer volume of textual content on web-based social media platforms can be overwhelming. These platforms generate an enormous amount of unstructured data every second. To gain insights into human psychology, it is imperative to process this data as quickly as it is produced. This can be achieved through sentiment analysis, an advanced technique called Transformer-based model (TBM) which discerns the polarity of text, determining whether the author holds a positive, negative, or neutral stance towards a subject, service, individual, or location. The performance of this model can vary based on factors like the dataset used, the specific Transformer variant, model hyper parameters, and the evaluation metrics employed. Findings from this study show that social media users with depression or anorexia may be identified by the presence and unpredictability of their emotions. The proposed model is used to analyze text data and make sentiment predictions effectively. The proposed TBM strategy illustrated predominant execution over distinctive measurements compared to other methods. For 50 clients, TBM accomplished an precision of 94.23%, accuracy of 89.13%, and review of 91.59%. As the client check expanded to 100, 150, 200, and 250, TBM reliably outflanked others, coming to up to 97.03% precision, 92.89% exactness, and 93.51% review. These comes about emphasize the viability of the TBM approach over elective strategies.
Suggested Citation
R. Tamilkodi & B. Sujatha & N. Leelavathy, 2025.
"Emotion detection in text: advances in sentiment analysis,"
International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(2), pages 552-560, February.
Handle:
RePEc:spr:ijsaem:v:16:y:2025:i:2:d:10.1007_s13198-024-02597-0
DOI: 10.1007/s13198-024-02597-0
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
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:ijsaem:v:16:y:2025:i:2:d:10.1007_s13198-024-02597-0. 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.
We have no bibliographic references for this item. You can help adding them by using 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.