Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

Akula, Ramya; Garibay, Iván

doi:10.20944/preprints202101.0302.v1

Cited by 15 publications

(7 citation statements)

References 28 publications

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“…Attention mechanism finds pattern in the input that is critical for resolving the provided process. In DL method, self-attention [18] is an attention method for sequence that assists in learning the task-specific relationships among distinct components of a provided sequence for producing a good sequence representation. During the self-attention model, 3 linear predictions: Key (K), Value (V), and Query (Q) of the provided input sequence is produced in which, Q;…”

Section: Mhsa-gru Based Sarcasm Classificationmentioning

confidence: 99%

Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

Sait¹,

Ishak²

2023

Computer Systems Science and Engineering

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Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking. The existence of sarcasm in textual data is a major challenge in the efficiency of the SA. Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm. With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature. In this aspect, this study designs a deep learning with natural language processing enabled SA (DLNLP-SA) technique for sarcasm classification. The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data. Besides, the DLNLP-SA technique holds various sub-processes namely preprocessing, feature vector conversion, and classification. Initially, the pre-processing is performed in diverse ways such as single character removal, multi-spaces removal, URL removal, stopword removal, and tokenization. Secondly, the transformation of feature vectors takes place using the N-gram feature vector technique. Finally, mayfly optimization (MFO) with multi-head self-attention based gated recurrent unit (MHSA-GRU) model is employed for the detection and classification of sarcasm. To verify the enhanced outcomes of the DLNLP-SA model, a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

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Section: Mhsa-gru Based Sarcasm Classificationmentioning

confidence: 99%

Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

Sait¹,

Ishak²

2023

Computer Systems Science and Engineering

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“…A better solution is lower error rate and the worst solution gains an enhanced error rate. Finally, a detailed comparison study of the ASDC-HPTDL model with existing techniques is in Table 2 (Akula & Garibay, 2021).…”

Section: Iafo Based Hyperparameter Tuningmentioning

confidence: 99%

Automated sarcasm detection and classification using hyperparameter tuned deep learning model for social networks

Vinoth

Paneer

2022

Expert Systems

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In recent digital era, social media sites have been commonly used by majority of people to generate massive quantities of textual data. Sarcasm can be treated as a kind of sentiment, which generally expresses the opposite of what has been anticipated. Since sarcasm detection is mainly based on the context of utterances or sentences, it is hard to design a model to proficiently detect sarcasm in the domain of natural language processing (NLP). The recent advancements of deep learning (DL) models influence neural networks (NN) in learning the lexical as well as contextual features, eradicating the necessity of hand-crafted features for sarcasm detection. With this motivation, this article designs an automated sarcasm detection and classification tool using hyperparameter tuned deep learning (ASDC-HPTDL) model for social media. The proposed ASDC-HPTDL technique primarily involves pre-processing stage to transform the data into useful format. At the next stage of pre-processing, the pre-processed data is converted into the feature vector by Glove Embedding's technique. Followed by, attention bidirectional gated recurrent unit (ABiGRU) technique is utilized to detect and classify sarcasm. In order to boost the detection outcomes of the ABiGRU technique, a hyperparameter tuning process using improved artificial flora algorithm (IAFO) is employed, shows the novelty of the work. The proposed model is validated using the benchmark dataset and the results are examined interms of precision, recall, accuracy, and F1-score.

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“…Particularly, they utilize bi-directional gated RNN for capturing syntactic and semantic data on twitters, and a pooling NN for extracting contextual features manually from past twitters. Akula and Garibay [17] concentrate on identifying sarcasm in textual conversation from different societal and online platforms for networking. Eventually, they developed an interpretable DL method with gated recurrent units and multihead self-attention.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Metaheuristic Optimization with Deep Convolutional Recurrent Neural Network Enabled Sarcasm Detection and Classification Model

Kavitha¹,

Chittineni²

2022

IJACSA

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Sarcasm is a state of speech in which the speaker says something that is externally unfriendly with a purpose of abusing/deriding the listener and/or a third person. Since sarcasm detection is mainly based on the context of utterances or sentences, it is hard to design a model to proficiently detect sarcasm in the domain of natural language processing (NLP). Despite the fact that various methods for detecting sarcasm have been created utilizing statistical machine learning and rule-based approaches, they are unable of discerning figurative meanings of words. The models developed using deep learning approaches have shown superior performance for sarcasm detection over traditional approaches. With this motivation, this paper develops novel deep learning (DL) enabled sarcasm detection and classification (DLE-SDC) model. The DLE-SDC technique primarily involves pre-processing stage which encompasses single character removal, multispaces removal, URL removal, stop word removal, and tokenization. Next to data preprocessing, the preprocessed data is converted into the feature vector by Glove Embeddings technique. Followed by, convolutional neural network with recurrent neural network (CNN-RNN) technique is utilized to detect and classify sarcasm. In order to boost the detection outcomes of the CNN+RNN technique, a hyper parameter tuning process utilizing teaching and learning based optimization (TLBO) algorithm is employed in such a way that the classification performance gets increased. The DLE-SDC model is validated using the benchmark dataset and the performance is examined interms of precision, recall, accuracy, and F1-score.

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Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

Cited by 15 publications

References 28 publications

Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

Automated sarcasm detection and classification using hyperparameter tuned deep learning model for social networks

An Intelligent Metaheuristic Optimization with Deep Convolutional Recurrent Neural Network Enabled Sarcasm Detection and Classification Model

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