Self-attention bidirectional long Short-Term memory assisted natural language processing on sarcasm detection and...

Area of Science:

• Natural Language Processing (NLP)
• Computational Linguistics
• Artificial Intelligence (AI)

Background:

• Sarcasm, a form of irony expressing negative opinions, presents a significant challenge in text analysis due to its reliance on implicit meaning.
• The prevalence of sarcasm on social media necessitates effective automated detection methods for improved human-computer interaction and information processing.
• Sarcasm detection is a crucial task in Natural Language Processing (NLP), driving research into advanced analytical techniques.

Purpose of the Study:

• To propose and evaluate a novel technique, Sarcasm Classification and Detection using NLP on Social Media Platforms (SDCNLP-SM), for the automated recognition of sarcastic text.
• To enhance the accuracy and efficiency of sarcasm detection models, particularly within the context of social media data.
• To contribute to the ongoing research in NLP by addressing the complexities of sarcasm identification.

Main Methods:

• The study employed a Sarcasm Classification and Detection using NLP on Social Media Platforms (SDCNLP-SM) technique, involving data preprocessing and Word2Vec word-embedding.
• A Self-Attention with Bidirectional Long Short-Term Memory (SA-BLSTM) model was utilized for the core sarcasm classification task.
• The methodology incorporated established NLP approaches, including deep learning (DL) and machine learning (ML) models, alongside Transformer architectures.

Main Results:

• The proposed SDCNLP-SM technique demonstrated a high accuracy rate of 94.45% in sarcasm classification on a headline dataset.
• Comparative analysis indicated that the SDCNLP-SM model significantly outperformed existing sarcasm detection models.
• The integration of Word2Vec embeddings and the SA-BLSTM architecture proved effective for nuanced sarcasm recognition.

Conclusions:

• The SDCNLP-SM technique offers a robust and accurate solution for automated sarcasm detection, especially on social media platforms.
• The findings highlight the effectiveness of combining advanced NLP techniques like SA-BLSTM with word-embedding methods for complex linguistic challenges.
• Further research can build upon this model to improve understanding and processing of sarcastic content across diverse online environments.