Zobrazeno 1 - 10
of 687
pro vyhledávání: '"Sarcasm detection"'
Publikováno v:
Journal of Communications Software and Systems, Vol 20, Iss 4, Pp 278-289 (2024)
Recent years have seen a notable rise in online opinion-sharing, underscoring the demand for automated sentiment analysis tools. Addressing sarcasm in text is crucial, as it can significantly influence the effectiveness of sentiment analysis models.
Externí odkaz:
https://doaj.org/article/6c94715d66ab46d590473204f5a5d265
Publikováno v:
Journal of Hospitality and Tourism Technology, 2024, Vol. 15, Issue 4, pp. 519-533.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JHTT-04-2023-0098
Publikováno v:
IEEE Access, Vol 12, Pp 87323-87332 (2024)
Sarcasm detection in the Indonesian language poses a unique set of challenges due to the linguistic nuances and cultural specificities of the Indonesian social media landscape. Understanding the dynamics of sarcasm in this context requires a deep div
Externí odkaz:
https://doaj.org/article/046bd1a172ef404fbbdcb2d71f51e1ad
Publikováno v:
Computer and Knowledge Engineering, Vol 7, Iss 1, Pp 49-58 (2024)
With the growing use of social media, figurative language has become very common on social media platforms. Given its complexity, figurative language can confuse natural language processing systems and lead to incorrect results. To address this issue
Externí odkaz:
https://doaj.org/article/8d426f4d2c77422fa77c02f908d7cedc
Autor:
Idrees A. Zahid, Shahad Sabbar Joudar, A.S. Albahri, O.S. Albahri, A.H. Alamoodi, Jose Santamaría, Laith Alzubaidi
Publikováno v:
Intelligent Systems with Applications, Vol 23, Iss , Pp 200431- (2024)
Large Language Models (LLMs) have become a hot topic in AI due to their ability to mimic human conversation. This study compares the open artificial intelligence generative pretrained transformer-4 (GPT-4) model, based on the (GPT), and Google's arti
Externí odkaz:
https://doaj.org/article/fbd30759f3e14b6990aaf4e908e39382
Publikováno v:
Array, Vol 22, Iss , Pp 100344- (2024)
With the rise of social media and online interactions, there is a growing need for analytical models capable of understanding the nuanced, multi-modal communication inherent in platforms, especially for detecting sarcasm. Existing research employs mu
Externí odkaz:
https://doaj.org/article/5eb5d33de270485c92afc1775803a229
Autor:
Maksim A. Kosterin, Ilya V. Paramonov
Publikováno v:
Моделирование и анализ информационных систем, Vol 31, Iss 1, Pp 90-101 (2024)
The paper examines automatic methods for classifying Russian-language sentences into two classes: ironic and non-ironic. The discussed methods can be divided into three categories: classifiers based on language model embeddings, classifiers using sen
Externí odkaz:
https://doaj.org/article/f377da69d72541bba106b0503ce683f2
Publikováno v:
IEEE Access, Vol 12, Pp 137063-137079 (2024)
Detecting sarcasm in text is a very challenging task. Sarcasm often depends on context, tone, and cultural references, which can be difficult for machines to understand. In addition, the increasing occurrence of code-mixing in social media posts pose
Externí odkaz:
https://doaj.org/article/32a2202da8d14a74a23aabca6e8106ca
Autor:
Muhammad Ehtisham Hassan, Masroor Hussain, Iffat Maab, Usman Habib, Muhammad Attique Khan, Anum Masood
Publikováno v:
IEEE Access, Vol 12, Pp 61542-61555 (2024)
Sarcasm has a significant role in human communication especially on social media platforms where users express their sentiments through humor, satire, and criticism. The identification of sarcasm is crucial in comprehending the sentiment and the comm
Externí odkaz:
https://doaj.org/article/debd2d3ae157468b8932f92aee64e07f
Publikováno v:
IEEE Access, Vol 12, Pp 38071-38080 (2024)
Sarcasm is a sophisticated speech act that is intended to express contempt or ridicule on social communities such as Twitter. In recent years, the prevalence of sarcasm on the social media has become highly disruptive to sentiment analysis systems du
Externí odkaz:
https://doaj.org/article/ebfe60c372744b1a843fd9d8808c4857