Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{173236,
        author = {Ankan Sinha and Sumit Gupta},
        title = {Author Profiling for Irony Detection based on Natural Language Processing Techniques.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2253-2257},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173236},
        abstract = {The project is concentrated on profiling ironic authors in Twitter. Special emphases are given to those authors that employ irony to spread stereotypes, as an example, towards the community. The goal is going to be to classify authors as ironic or not reckoning on their number of tweets with ironic content. Therefore, given authors of Twitter along with their tweets, the goal is going to be to profile those authors who will be considered as ironic. Here during this project, various ML models are accustomed verify the info provided supported Homonyms to detect the Ironical users and distinguished them from the non-Ironical users, thus have obtained an optimal result which encourage us to proceed further within the near future supported this approach of using homonyms to represent an Ironical situation.},
        keywords = {Twitter, Irony, Homonyms, Machine Learning, LR, SVM, Accuracy},
        month = {February},
        }