Sarcasm detection is challenging for both humans and machines. This work explores how model characteristics impact sarcasm detection in OpenAI's GPT, and Meta's Llama-2 models, given their strong natural language understanding, and popularity. We evaluate fine-tuned and zero-shot models across various sizes, releases, and hyperparameters. Experiments were conducted on the political and balanced (pol-bal) portion of the popular Self-Annotated Reddit Corpus (SARC2.0) sarcasm dataset. Fine-tuned performance improves monotonically with model size within a model family, while hyperparameter tuning also impacts performance. In the fine-tuning scenario, full precision Llama-2-13b achieves state-of-the-art accuracy and $F_1$-score, both measured at 0.83, comparable to average human performance. In the zero-shot setting, one GPT-4 model achieves competitive performance to prior attempts, yielding an accuracy of 0.70 and an $F_1$-score of 0.75. Furthermore, a model's performance may increase or decline with each release, highlighting the need to reassess performance after each release.

Sarcasm is hard to interpret as human beings. Being able to interpret sarcasm is often termed as a sign of intelligence, given the complex nature of sarcasm. Hence, this is a field of Natural Language Processing which is still complex for computers to decipher. This Literature Survey delves into different aspects of sarcasm detection, to create an understanding of the underlying problems faced during detection, approaches used to solve this problem, and different forms of available datasets for sarcasm detection.

Sarcasm is a form of irony that requires readers or listeners to interpret its intended meaning by considering context and social cues. Machine learning classification models have long had difficulty detecting sarcasm due to its social complexity and contradictory nature. This paper explores the applications of the Generative Pretrained Transformer (GPT) models, including GPT-3, InstructGPT, GPT-3.5, and GPT-4, in detecting sarcasm in natural language. It tests fine-tuned and zero-shot models of different sizes and releases. The GPT models were tested on the political and balanced (pol-bal) portion of the popular Self-Annotated Reddit Corpus (SARC 2.0) sarcasm dataset. In the fine-tuning case, the largest fine-tuned GPT-3 model achieves accuracy and $F_1$-score of 0.81, outperforming prior models. In the zero-shot case, one of GPT-4 models yields an accuracy of 0.70 and $F_1$-score of 0.75. Other models score lower. Additionally, a model's performance may improve or deteriorate with each release, highlighting the need to reassess performance after each release.

Sarcasm can be difficult to detect in text, especially for machines. However, with the power of large language models, it's possible to create a tool that can identify sarcastic comments with high accuracy. That's exactly what the ClearML team did with their latest project: a sarcasm detector that combines various ClearML tools to showcase the capabilities of MLOps. In the age of chatGPT and proprietary APIs, this project is meant as an example of how to create tools based on large language models that can run on your own machine, so you have full control over it. And thanks to ClearML being open source, even the whole MLOps stack can run locally.

As we all know, Artificial Intelligence and Machine Learning are transforming the world. It has numerous applications in various fields, from medical science to video games. Areas such as E-Commerce and Social media have used AI lucratively and have benefited the most. However, in this project, I decided to use AI for a fun task. I tried to build a model to detect sarcasm in a text.

So all the all the previous achievements by different machine learning algorithms, precisely the reinforcement learning algorithms, is that it could look at …

Researchers from the University of Central Florida (UCF) have developed a sarcasm detector for social media platforms using artificial intelligence. According to Ramya Akula, sarcasm in face-to-face interactions is easier to determine, one of the researchers of the study. However, the lack of facial expressions and vocal tones in Facebook posts and Tweets makes it more tedious to catch. What more, if a computer attempts to figure it out. "Sarcasm detection in online communications from social networking platforms is much more challenging," Ramya said, though UCF's website. The social measures brought upon the Covid 19 pandemic have forced most people to interact online.

Social media has become a dominant form of communication for individuals, and for companies looking to market and sell their products and services. Properly understanding and responding to customer feedback on Twitter, Facebook and other social media platforms is critical for success, but it is incredibly labor intensive. That's where sentiment analysis comes in. The term refers to the automated process of identifying the emotion -- either positive, negative or neutral -- associated with text. While artificial intelligence refers to logical data analysis and response, sentiment analysis is akin to correctly identifying emotional communication.

A new AI tool funded in part by the U.S. military has proven adept at a task that has traditionally been very difficult for computer programs: detecting the human art of sarcasm. It could help intelligence officers or agencies better apply artificial intelligence to trend analysis by avoiding social media posts that aren't serious. Certain words in specific combinations can be a predictable indicator of sarcasm in a social media post, even if there isn't much other context, two researchers from the University of Central Florida noted in a March paper in the journal Entropy. Using a variety of datasets of posts from Twitter, Reddit, various dialogues and even headlines from The Onion, Garibay and his colleague Ramya Akula mapped out how some key words relate to other words. "For instance, words such as'just', 'again', 'totally', '!', have darker edges connecting them with every other word in a sentence. These are the words in the sentence that hint at sarcasm and, as expected, these receive higher attention than others," they write.

No second thoughts about the fact that how critical has social media become a part of our lives. We rely on social media so much that today imagining a life without it does not sink in. No wonder social media is considered to be one of the best platforms to market and sell different products and services in addition to being a dominant form of communication. With this platform, you stand a chance to reach out to the maximum lot. While this medium is used for driving sales, another area that it caters to is how are our customers reacting to what you are delivering.