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Exploring Upper Limb Kinematics in Limited Vision Conditions: Preliminary Insights from 3D Motion Analysis and IMU Data
Artemis Zarkadoula, Themistoklis Tsatalas, George Bellis, Paris Papaggelos, Evangelia Vlahogianni, Stefanos Moustos, Eirini Koukourava and Dimitrios Tsaopoulos
Abstract

要約 at IgMin Research

Our aspiration is to merge expertise across disciplines to accelerate scientific discovery.

Engineering Group Mini Review 記事ID: igmin137

A Capsule Neural Network (CNN) based Hybrid Approach for Identifying Sarcasm in Reddit Dataset

Machine Learning Cybersecurity Affiliation

Affiliation

    Department of Electronic Engineering, Jeju National University, Jeju-si, Jeju-do, Republic of Korea, 63243

要約

Sarcasm, a standard social media message, delivers the opposite meaning through irony or teasing. Unfortunately, identifying sarcasm in written text is difficult in natural language processing. The work aims to create an effective sarcasm detection model for social media text data, with possible applications in sentiment analysis, social media analytics, and online reputation management. A hybrid Deep learning strategy is used to construct an effective sarcasm detection model for written content on social media networks. The design emphasizes feature extraction, selection, and neural network application. Limited research exists on detecting sarcasm in human speech compared to emotion recognition. The study recommends using Word2Vec or TF-IDF for feature extraction to address memory and temporal constraints. Use feature selection techniques like PCA or LDA to enhance model performance by selecting relevant features. A Capsule Neural Network (CNN) and Long Short-Term Memory (LSTM) collect contextual information and sequential dependencies in textual material. We evaluate Reddit datasets with labelled sarcasm data using metrics like Accuracy. Our hybrid method gets 95.60% accuracy on Reddit.

数字

参考文献

    1. Shubham R, Chandankhede C. Sarcasm detection of online comments using emotion detection. 2018 International conference on inventive research in computing applications (ICIRCA). IEEE, 2018.
    2. Vitman O, Kostiuk Y, Sidorov G, Gelbukh A. Sarcasm detection framework using context, emotion, and sentiment features. Expert Systems with Applications. 2023; 234: 121068.
    3. Šandor D, Babac MB. Sarcasm detection in online comments using machine learning. Information Discovery and Delivery, (ahead-of-print). 2023.
    4. Pandey R, Singh JP. BERT-LSTM model for sarcasm detection in code-mixed social media posts. Journal of Intelligent Information Systems. 2023; 60(1): 235-254.
    5. Tan YY, Chow CO, Kanesan J, Chuah JH, Lim Y. Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning. Wirel Pers Commun. 2023;129(3):2213-2237. doi: 10.1007/s11277-023-10235-4. Epub 2023 Mar 4. PMID: 36987507; PMCID: PMC9985100.
    6. Pulkit M, Soni D. Identification of sarcasm using word embeddings and hyperparameters tuning. Journal of Discrete Mathematical Sciences and Cryptography. 2019; 22.4: 465-489.
    7. Qiao Y, Jing L, Song X, Chen X, Zhu L, Nie L. Mutual-enhanced incongruity learning network for multi-modal sarcasm detection. In Proceedings of the AAAI Conference on Artificial Intelligence. 2023; 37: 9507-9515.
    8. Usman N. Towards improved deep contextual embedding for the identification of irony and sarcasm. 2020 International joint conference on neural networks (IJCNN). IEEE. 2020.
    9. Aniruddha G, Veale T. Fracking sarcasm using neural network. Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment, and social media analysis. 2016.
    10. Zhang M, Zhang Y, Fu G. Tweet sarcasm detection using deep neural network. In Matsumoto Y., Prasad R. (Eds.), Proceedings of COLING 2016, the 26th international conference on computational linguistics: Technical papers. 2016; 2449–2460.
    11. Alexandros PR, Siolas G, Stafylopatis AG. A transformer-based approach to irony and sarcasm detection. Neural Computing and Applications. 2020; 32: 17309-17320.
    12. Yi T. Reasoning with sarcasm by reading in-between. arXiv preprint arXiv:1805.02856 (2018).
    13. Avinash K. Adversarial and auxiliary features-aware bert for sarcasm detection. Proceedings of the 3rd ACM India Joint International Conference on Data Science Management of Data (8th ACM IKDD CODS 26th COMAD). 2021.

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