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Twitter Roberta Base Irony



The twitter-roBERTa-base-irony model is a text classification model based on the roBERTa-base architecture. It has been trained on a large dataset of tweets and fine-tuned specifically for irony detection. The model performs well on the TweetEval benchmark for irony detection.

Use cases

1. Social media monitoring: This AI model can be used by companies and organizations to monitor social media platforms for instances of irony. It can help them identify sarcastic or ironic comments made by users, allowing them to gain insights into public opinion, customer sentiment, and potential risks or opportunities. 2. Brand management: The model can be utilized by brands to analyze and understand how their products or services are perceived by customers on social media. By detecting irony, companies can identify instances where customers might be expressing dissatisfaction or frustration in a covert manner, allowing them to address issues and improve customer satisfaction. 3. News analysis: Journalists and media organizations can leverage this model to analyze and categorize tweets related to news topics. Irony often plays a role in discussions around news events, and this AI model can help identify sarcastic or satirical comments, providing a deeper understanding of public perception and sentiment towards news stories. 4. Sentiment analysis: Sentiment analysis is an important application of natural language processing, and this model can be used to enhance existing sentiment analysis systems. By accurately detecting irony, sentiment analysis models can avoid misinterpreting sarcastic or ironic comments, leading to more accurate sentiment analysis results. Possible products and practical uses: 1. Social media monitoring tool: A software platform that utilizes this model to provide real-time monitoring and analysis of social media platforms. It could help companies track their brand reputation, identify potential crises, and gain actionable insights from social media conversations. 2. News sentiment analysis service: A service that analyzes tweets related to news topics and provides insights on the sentiment, including detecting the presence of irony. This could help media organizations and journalists measure public opinion and gauge the impact of news stories. 3. Customer feedback analysis tool: A tool that combines sentiment analysis with irony detection to provide more accurate and nuanced analysis of customer feedback, allowing companies to identify areas for improvement and proactively address customer concerns. These are just some of the possible use cases and product ideas that can be derived from this AI model. The versatility and accuracy of the twitter-roBERTa-base-irony model make it a valuable asset for a wide range of applications in the realm of text classification and sentiment analysis.



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Creator Models

Roberta Base Emoji$?20
Twitter Roberta Base Dec2021 Emotion$?24
Twitter Roberta Base Dec2021 Hate$?11
Twitter Roberta Base Dec2021 Sentiment$?11
Twitter Roberta Base 2021 124m Hate$?36

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Summary of this model and related resources.

Model NameTwitter Roberta Base Irony

Twitter-roBERTa-base for Irony Detection This is a roBERTa-...

Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided


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