Sarcamnet: Extension of Lexicon Algorithm For Emoji-Based Sarcasm Detection From Twitter Data

Abstract

Sarcamnet is an advanced system designed to detect sarcasm in Twitter data by extending the traditional Lexicon Algorithm with emoji sentiment analysis. Recognizing the growing use of emojis to convey sarcasm in social media, Sarcamnet incorporates a sentiment-aware emoji lexicon that allows it to accurately analyze both text and emojis in real time. By understanding the contextual interaction between positive or neutral text and negative emojis (e.g., "Oh great… another Monday 😒"), the system can more effectively identify sarcastic tweets. Real-time sarcasm detection from large volumes of tweets, processing both textual and emoji content simultaneously. An adaptive lexicon that evolves with the changing trends in emoji use and slang, ensuring continuous accuracy in sentiment analysis. Enhanced ability to reduce false positives and false negatives, making the system highly reliable for social media monitoring, customer feedback analysis, and brand sentiment tracking. Sarcamnet is designed to provide accurate, scalable, and adaptable sarcasm detection, offering a more nuanced understanding of social media sentiment. Its integration of emoji sentiment and text analysis makes it a valuable tool for businesses, researchers, and analysts looking to gain deeper insights into public opinion and customer attitudes on social platforms like Twitter.

Specification

Description:Sarcamnet in Fig. 1 is an innovative system designed to detect sarcasm in Twitter data by extending the traditional Lexicon Algorithm with emoji-based sentiment analysis. The system is specifically tailored for social media, where sarcasm often appears in the form of tweets combining both text and emojis.
Key Components and Features:
1. Lexicon-Based Sarcasm Detection:
o The system builds upon the Lexicon Algorithm, which uses predefined lists of words and phrases that are typically associated with sarcasm. It recognizes sarcasm based on the context and tone of the text, leveraging a comprehensive lexicon to identify when a tweet's meaning is opposite of its literal words.
2. Emoji Sentiment Integration:
o Emoji sentiment mapping is a core addition to the traditional lexicon-based approach. Sarcamnet incorporates a sentiment-aware emoji lexicon, understanding the emotional weight of emojis like 😒, 😏, or 🙄, which often accompany sarcastic comments. By analyzing both the text and the accompanying emojis, the system can more accurately detect when sarcasm is present.
3. Contextual Analysis:
o Sarcamnet analyzes the relationship between text and emojis to understand whether they are reinforcing each other or contradicting each other. For example, a tweet like "Fantastic weather today 😒" would be flagged as sarcastic because the text suggests positivity, but the emoji implies frustration or dissatisfaction.
4. Real-Time Data Processing:
o The system processes real-time Twitter data, monitoring tweets as they are posted to quickly detect sarcasm in large volumes of social media content. This makes it useful for applications such as brand monitoring, customer feedback analysis, and social sentiment tracking.
5. Adaptable Lexicon and Emoji Database:
o The lexicon and emoji sentiment mapping are adaptable and can be updated as new emojis or slang terms emerge in social media communication, ensuring that the system remains up-to-date with evolving language and emoji usage.
The operational performance of the Sarcamnet system is focused on ensuring accuracy, speed, and scalability in detecting sarcasm within tweets that combine both text and emojis. Below are the key aspects of Sarcamnet's operational performance:
1. High Accuracy in Sarcasm Detection
• Text and Emoji Analysis: Sarcamnet enhances the traditional lexicon-based sarcasm detection by incorporating emoji sentiment mapping, allowing it to detect sarcasm that might be missed when analyzing text alone. By understanding the contextual relationship between words and emojis (e.g., a positive phrase followed by a negative emoji like 🙄), the system achieves greater accuracy in sarcasm detection.
• Context Sensitivity: The system is trained to recognize when emojis and text are working in contradiction (a common sign of sarcasm), helping it to distinguish between genuine sentiment and sarcastic tone effectively.
, C , Claims:
1. We claim that this system improves the accuracy of sarcasm detection in social media sentiment analysis,
2. We claim that the invention provides a more comprehensive and nuanced understanding of sarcasm.
3. We claim that the invention ensures that it accurately detects sarcasm even as communication styles on Twitter evolve.
4. We claim that the system improves the overall effectiveness of sentiment analysis for businesses, marketers, and researchers
5. We claim that this method optimizes the system's precision and recall to ensure a balance between detecting sarcasm accurately

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