Ren et al 2023b - Revision history

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Rimni: /* 8. Future work */

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‎8. Future work

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 {| style="text-align: center; margin:auto;width: 100%;"
 |-
-| style="text-align: center;" |<math>\hbox{RMSE }=\sqrt{\sum _{}^{}{(\hbox{predicted-acutal})}^{2}/2}</math>
+| style="text-align: center;" |<math>\hbox{RMSE }=\sqrt{\sum _{}^{}{(\hbox{predicted-actual})}^{2}/2}</math>
 |}
 | style="width: 5px;text-align: right;white-space: nowrap;" | (4)

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 ==5. Datasets and languages==
-In the literature we surveyed, there are two types of dataset sources for humor recognition, the first in which is data collected on its own data to the requirements of the task, and the second using public datasets in [[#tab-2|Table 2]]. The first case contain 10 papers, which are [15,17,20,21,24,25,26,[27,41,42]. Some of this self-collected data comes from Twitter, some from other sites. The remaining papers use the detail of public datasets. The most frequently used public datasets are Passau-SFCH, UR-FUNNY, Pun of the Day and the 16000 One-liner.
+In the literature we surveyed, there are two types of dataset sources for humor recognition, the first in which is data collected on its own data to the requirements of the task, and the second using public datasets in [[#tab-2|Table 2]]. The first case contain 10 papers, which are [15,17,20,21,24,25,26,27,41,42]. Some of this self-collected data comes from Twitter, some from other sites. The remaining papers use the detail of public datasets. The most frequently used public datasets are Passau-SFCH, UR-FUNNY, Pun of the Day and the 16000 One-liner.
 <div class="center" style="font-size: 75%;">'''Table 2'''. Summary of dataset from surveyed articles</div>

@@ Line 97: / Line 97: @@
 Xiong et al. [33] proposed a humor identification model (MLSN) using popular humor theory and deep learning approaches for humor task. By incorporating the incoherence, phonetic properties and vagueness of a humorous statement as semantic attributes, the model automatically recognizes whether a statement includes a humorous utterance. The results show that the new model has better humor recognition accuracy and can contribute to discourse understanding research on three datasets.
-Christ et al. [34] have three datasets for this Multimodal Sentiment Analysis Challenge (MuSe) 2022, the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset, the Hume-Reaction dataset, the Ulm-Trier Social Stress Test (Ulm-TSST) dataset. For each sub-challenge, a deep learning recurrent neural network with LSTM cells was employed to determine benchmark on the test divisions. They recorded an area under the curve (AUC) of .8480 for MuSe humor; a mean (from 7 classes) Pearson's correlation coefficient (ð) of .2801 for MuSe response, as well as a concordance correlation coefficient (CCC) of .4931 and .4761 for valence and arousal in MuSe stress, respectively.
+Christ et al. [34] have three datasets for this Multimodal Sentiment Analysis Challenge (MuSe) 2022, the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset, the Hume-Reaction dataset, the Ulm-Trier Social Stress Test (Ulm-TSST) dataset. For each sub-challenge, a deep learning recurrent neural network with LSTM cells was employed to determine benchmark on the test divisions. They recorded an area under the curve (AUC) of .8480 for MuSe humor; a mean (from 7 classes) Pearson's correlation coefficient (<math display="inline"> \delta</math>) of .2801 for MuSe response, as well as a concordance correlation coefficient (CCC) of .4931 and .4761 for valence and arousal in MuSe stress, respectively.
 To aid humor understanding, they elaborated on incongruity and ambiguity and suggested an internal/external attention neural network (IEANN) for humor recognition [35]. To address incongruity and ambiguity in humorous texts, IEANN combined two different types of attentional mechanisms. At the same time, in order to verify the performance and reliability of the model, extensive experiments were conducted on two humor datasets.

@@ Line 739: / Line 739: @@
 [24] Chen R., Rau P.-L.P. Deep learning model for humor recognition of different cultures. In Cross-Cultural Design. Experience and Product Design Across Cultures, 13th International Conference, CCD 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part I 23 Springer International Publishing, 373-389, 2021.
-[25] Prajapati P., Jaiswal A., Aastha, Shilpi, Neha, Sachdeva N.  Empirical Analysis of Humor Detection Using Deep Learning and Machine Learning on Kaggle Corpus, International Conference on Advancements in Interdisciplinary Research:300-312. 2022.
+[25] Prajapati P., Jaiswal A., Aastha, Shilpi, Neha, Sachdeva N.  Empirical analysis of humor detection using deep learning and machine learning on kaggle corpus. AIR 2022: International Conference on Advancements in Interdisciplinary Research, 300-312, 2022.
-[26] Li D., Rzepka R., Ptaszynski M., Araki K. HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media, Information Processing & Management, 57 (6). 2020.
+[26] Li D., Rzepka R., Ptaszynski M., Araki K. HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media. Information Processing & Management, 57(6), 102290, 2020.
-[27] Ziser Y., Kravi E., Carmel D. Humor Detection in Product Question Answering Systems, Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 519-528, 2020.
+[27] Ziser Y., Kravi E., Carmel D. Humor detection in product question answering systems. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 519-528, 2020.
-[28] Mahajan R., Zaveri M. Humor identification using affect based content in target text, Journal of Intelligent & Fuzzy Systems, 39 (1): 697-708. 2020.
+[28] Mahajan R., Zaveri M. Humor identification using affect based content in target text. Journal of Intelligent & Fuzzy Systems, 39(1) 697-708, 2020.
-[29] Hasan M.K., Lee S., Rahman W., Zadeh A. Humor Knowledge Enriched Transformer for Understanding Multimodal Humor, In Proceedings of the AAAI conference on artificial intelligence, 12972-12980, 2021.
+[29] Hasan M.K., Lee S., Rahman W., Zadeh A. Humor knowledge enriched transformer for understanding multimodal humor. In Proceedings of the AAAI Conference on Artificial Intelligence, 12972-12980, 2021.
-[30] Yang D., Lavie A., Dyer C., Hovy E. Humor Recognition and Humor Anchor Extraction, Proceedings of the 2015 conference on empirical methods in natural language processing, 2367-2376, 2015.
+[30] Yang D., Lavie A., Dyer C., Hovy E. Humor recognition and humor anchor extraction. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2367-2376, 2015.
-[31] Xu H., Liu W., Liu J., Li M., Feng Y., Peng Y., Shi Y., Sun X., Wang M. Hybrid Multimodal Fusion for Humor Detection, Proceedings of the 3rd International on Multimodal Sentiment Analysis Workshop and Challenge, 15-21, 2022.
+[31] Xu H., Liu W., Liu J., Li M., Feng Y., Peng Y., Shi Y., Sun X., Wang M. Hybrid multimodal fusion for humor detection. Proceedings of the 3rd International on Multimodal Sentiment Analysis Workshop and Challenge, 15-21, 2022.
-[32] Hasan M.K., Rahman W., Zadeh A., Zhong J. UR-FUNNY A Multimodal Language Dataset for Understanding Humor, arXiv preprint arXiv:1904.06618. 2019.
+[32] Hasan M.K., Rahman W., Zadeh A., Zhong J. UR-FUNNY A multimodal language dataset for understanding humor. arXiv preprint arXiv:1904.06618, 2019.
-[33] Xiong S., Wang R., Huang X., Chen Z. Multidimensional Latent Semantic Networks for Text Humor Recognition, Sensors (Basel), 22 (15). 2022.
+[33] Xiong S., Wang R., Huang X., Chen Z. Multidimensional latent semantic networks for text humor recognition. Sensors (Basel), 22(15), 5509, 2022.
-[34] Christ L., Amiriparian S., Baird A., Tzirakis P., Kathan A., Müller N., Stappen L., Meßner E.-M., König A., Cowen A., Cambria E., Schuller B.W. The MuSe 2022 Multimodal Sentiment Analysis Challenge, Proceedings of the 3rd International on Multimodal Sentiment Analysis Workshop and Challenge, 5-14, 2022.
+[34] Christ L., Amiriparian S., Baird A., Tzirakis P., Kathan A., Müller N., Stappen L., Meßner E.-M., König A., Cowen A., Cambria E., Schuller B.W. The MuSe 2022 multimodal sentiment analysis challenge. Proceedings of the 3rd International on Multimodal Sentiment Analysis Workshop and Challenge, 5-14, 2022.
-[35] Fan X., Lin H., Yang L., Diao Y., Shen C., Chu Y., Zou Y. Humor detection via an internal and external neural network, Neurocomputing, 394 105-111. 2020.
+[35] Fan X., Lin H., Yang L., Diao Y., Shen C., Chu Y., Zou Y. Humor detection via an internal and external neural network. Neurocomputing, 394:105-111, 2020.
-[36] Peng-Yu C., Von-Wun S. Humor recognition using deep learning, Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: Human language technologies,  2018.
+[36] Peng-Yu C., Von-Wun S. Humor recognition using deep learning. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,  vol. 2, 113–117, 2018.
-[37] Fan X., Lin H., Yang L., Diao Y., Shen C., Chu Y., Zhang T. Phonetics and Ambiguity Comprehension Gated Attention Network for Humor Recognition, Complexity, 1-9. 2020.
+[37] Fan X., Lin H., Yang L., Diao Y., Shen C., Chu Y., Zhang T. Phonetics and ambiguity comprehension gated attention network for humor recognition. Complexity, 2020:1-9, 2020.
-[38] Chen C., Zhang P. Integrating Cross-modal Interactions via Latent Representation Shift for Multi-modal Humor Detection, Proceedings of the 3rd International on Multimodal Sentiment Analysis Workshop and Challenge, 23-28, 2022.
+[38] Chen C., Zhang P. Integrating cross-modal interactions via latent representation shift for multi-modal humor detection. Proceedings of the 3rd International on Multimodal Sentiment Analysis Workshop and Challenge, 23-28, 2022.
-[39] Mondal A., Sharma R. Team KGP at SemEval-2021 Task 7: A Deep Neural System to Detect, Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021),  2021.
+[39] Mondal A., Sharma R. Team KGP at SemEval-2021 Task 7: A deep neural system to detect. Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), 1169–1174, 2021.
-[40] Wei H., Hui C., Alexander G., Amir Z. Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis, Proceedings of the 2021 International Conference on Multimodal Interaction,  2021.
+[40] Wei H., Hui C., Alexander G., Amir Z. Bi-bimodal modality fusion for correlation-controlled multimodal sentiment analysis. Proceedings of the 2021 International Conference on Multimodal Interaction,  6–15, 2021.
-[41] Arunima J., Monika, Mathur A., Prachi  Automatic Humour Detection in Tweets using Soft Computing Paradigms, 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (Com-IT-Con), India. 2019.
+[41] Arunima J., Monika, Mathur A., Prachi, Sheena Ma.  Automatic humour detection in tweets using soft computing paradigms. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (Com-IT-Con), Faridabad, India, 172-176, 2019.
-[42] Chen P.-Y., Soo V.-W. Humor recognition using deep learning, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 113-117, 2018.
+[42] Chen P.-Y., Soo V.-W. Humor recognition using deep learning. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 113-117, 2018.
-[43] Burak A.T., Murat G., Ibrahim T. Database for an emotion recognition system based on EEG signals and various computer games–GAMEEMO, Biomedical Signal Processing Control, 60. 101951. 2020.
+[43] Burak A.T., Murat G., Ibrahim T. Database for an emotion recognition system based on EEG signals and various computer games–GAMEEMO. Biomedical Signal Processing Control, 60, 101951, 2020.
-[44] Santiago C., Devamanyu H., Verónica P.-R., Roger Z., Rada M., Poria S. Towards multimodal sarcasm detection, arXiv preprint arXiv:.01815. 2019.
+[44] Santiago C., Devamanyu H., Verónica P.-R., Roger Z., Rada M., Poria S. Towards multimodal sarcasm detection. arXiv preprint arXiv:.01815, 2019.

← Older revision		Revision as of 09:14, 31 January 2024
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	==References==		==References==
		+
		+	<div class="auto" style="text-align: left;width: auto; margin-left: auto; margin-right: auto;font-size: 85%;">
		+

	[1] Ramakristanaiah C., Namratha P., Ganiya R.K., Reddy M.R. A survey on humor detection methods in communications. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), IEEE, 668-674, 2021.		[1] Ramakristanaiah C., Namratha P., Ganiya R.K., Reddy M.R. A survey on humor detection methods in communications. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), IEEE, 668-674, 2021.

@@ Line 696: / Line 696: @@
 [4] Attardo S. Irony as relevant inappropriateness. Journal of Pragmatics, 32(6):793-826. 2000.
-[5] Shahe K. Humour styles, personality, and well‐being among Lebanese university students, European journal of Personality, 18 (3): 209-219. 2004.
+[5] Shahe K. Humour styles, personality, and well‐being among Lebanese university students. European Journal of Personality, 18(3):209-219, 2004.
-[6] Sonja U. The function of self-disclosure on social network sites: Not only intimate, but also positive and entertaining self-disclosures increase the feeling of connection, Computers in Human Behavior, 45 1-10. 2015.
+[6] Sonja U. The function of self-disclosure on social network sites: Not only intimate, but also positive and entertaining self-disclosures increase the feeling of connection. Computers in Human Behavior, 45:1-10, 2015.
-[7] Tajfel H., Turner J.C., Austin W.G., Worchel S. An integrative theory of intergroup conflict, Organizational identity: A reader, 56 (65): 9780203505984-16. 1979.
+[7] Tajfel H., Turner J.C., Austin W.G., Worchel S. An integrative theory of intergroup conflict. Organizational Identity: A reader, 56(65):9780203505984-16, 1979.
-[8] Martin R.A., Ford T.  The psychology of humor: An integrative approach, Academic press.2018.
+[8] Martin R.A., Ford T.  The psychology of humor: An integrative approach. Academic Press, 2018.
-[9] Giora R. Understanding figurative and literal language: The graded salience hypothesis, The graded salience hypothesis. 1997.
+[9] Giora R. Understanding figurative and literal language: The graded salience hypothesis. Cognitive Linguistics, 8(3):183-206, 1997.
-[10] Gibbs R.W.  The poetics of mind: Figurative thought, language, and understanding, Cambridge University Press.1994.
+[10] Gibbs R.W.  The poetics of mind: Figurative thought, language, and understanding. Cambridge University Press, 1994.
-[11] Clark H.H.  Using language, Cambridge university press.1996.
+[11] Clark H.H.  Using language. Cambridge University Press, 1996.
-[12] Purcell D., Brown M.S., Gokmen M. Achmed the dead terrorist and humor in popular geopolitics, Achmed the dead terrorist and humor in popular geopolitics, 75 373-385. 2010.
+[12] Purcell D., Brown M.S., Gokmen M. Achmed the dead terrorist and humor in popular geopolitics. GeoJournal 75:373–385, 2010.
-[13] Mao J., Liu W. A BERT-based Approach for Automatic Humor  Detecting and Scoring, In [mailto:IberLEF@ IberLEF@] SEPLN, 197-202. 2019.
+[13] Mao J., Liu W. A BERT-based approach for automatic humor  detecting and scoring. In [mailto:IberLEF@ IberLEF@] SEPLN, 197-202, 2019.
-[14] Heaton J., Givigi S. A Deep CNN System for Classification of Emotions Using EEG Signals, 2022 IEEE International Systems Conference (SysCon), 1-7, 2022.
+[14] Heaton J., Givigi S. A deep CNN system for classification of emotions using EEG signals. 2022 IEEE International Systems Conference (SysCon),  Montreal, QC, Canada, 1-7, 2022.
-[15] Xie J., Tang M., Xiong J. A Multimodal Chinese Humor Classification Algorithm Based on Interactive Attention and Text and Speech Fusion, Available at SSRN 4241914. 2022.
+[15] Xie J., Tang M., Xiong J. A multimodal Chinese humor classification algorithm based on interactive attention and text and speech fusion. Available at SSRN 4241914, 2022.
-[16] Kathan A., Amiriparian S., Christ L., Triantafyllopoulos A., Müller N., König A., Schuller B.W.  A Personalised Approach to Audiovisual Humour Recognition and its Individual-level Fairness, Proceedings of the 3rd International on Multimodal Sentiment Analysis Workshop and Challenge:29-36. 2022.
+[16] Kathan A., Amiriparian S., Christ L., Triantafyllopoulos A., Müller N., König A., Schuller B.W.  A personalised approach to audiovisual humour recognition and its individual-level fairness. Proceedings of the 3rd International on Multimodal Sentiment Analysis Workshop and Challenge, 29-36, 2022.
-[17] Godoy F.C.D. Advancing Humor-Focused Sentiment Analysis through improved contextualized, 34th Conference on Neural Information Processing Systems (NeurIPS 2020),  2020.
+[17] Godoy F.C.D. Advancing humor-focused sentiment analysis through improved contextualized. 34th Conference on Neural Information Processing Systems (NeurIPS 2020),  2020.
-[18] Chauhan D.S., R D.S., Ekbal A., Bhattacharyya P. All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes, Proceedings ofthe 1st Conference ofthe Asia-Pacific Chapter ofthe Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, 281-290, 2020.
+[18] Chauhan D.S., R D.S., Ekbal A., Bhattacharyya P. All-in-One: A deep attentive multi-task learning framework for humour, sarcasm, offensive, motivation, and sentiment on memes. Proceedings ofthe 1st Conference ofthe Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, 281-290, 2020.
-[19] Xu Z., Xie Y. Attention Method Analysis in Sentiment Analysis for Humor Level Evaluation, 2022 14th International Conference on Computer Research and Development (ICCRD), 178-185, 2022.
+[19] Xu Z., Xie Y. Attention method analysis in sentiment analysis for humor level evaluation. 2022 14th International Conference on Computer Research and Development (ICCRD), 178-185, 2022.
-[20] Francesco Barbieri, Saggion H. Automatic Detection of Irony and Humour in Twitter, ICCC. 2014.
+[20] Barbieri F., Saggion H. Automatic detection of irony and humour in twitter. ICCC, 2014.
-[21] Annamoradnejad I. ColBERT Using BERT Sentence Embedding for Humor Detection, arXiv preprint arXiv:2004.12765 1.3. 2021.
+[21] Annamoradnejad I. ColBERT using BERT sentence embedding for humor detection. arXiv preprint arXiv:2004.12765 1.3,  2021.
-[22] Wang C., Xin S., Yi M., Zhang Z.  Comparative study on deep learning models in humor detection, 2021 International Conference on Neural Networks, Information and Communication Engineering. 2021.
+[22] Wang C., Xin S., Yi M. Comparative study on deep learning models in humor detection. Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021.
-[23] Dario B., Fung P. Deep Learning of Audio and Language Features for Humor Prediction, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16),  2016.
+[23] Dario B., Fung P. Deep learning of audio and language features for humor prediction. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16),  496–501, 2016.
-[24] Chen R., Rau P.-L.P. Deep Learning Model for Humor Recognition of Different Cultures, In Cross-Cultural Design. Experience and Product Design Across Cultures: 13th International Conference, CCD 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part I 23 Springer International Publishing, 373-389, 2021.
+[24] Chen R., Rau P.-L.P. Deep learning model for humor recognition of different cultures. In Cross-Cultural Design. Experience and Product Design Across Cultures, 13th International Conference, CCD 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part I 23 Springer International Publishing, 373-389, 2021.
 [25] Prajapati P., Jaiswal A., Aastha, Shilpi, Neha, Sachdeva N.  Empirical Analysis of Humor Detection Using Deep Learning and Machine Learning on Kaggle Corpus, International Conference on Advancements in Interdisciplinary Research:300-312. 2022.

@@ Line 155: / Line 155: @@
 |  style="vertical-align: top;"|Finding a mechanism to adapt to the characteristics of each individual
 |  style="vertical-align: top;"|Extending the method with imbalanced datasets
-|  style="vertical-align: top;"|the Passau Spontaneous Football Coach Humour (Passau-SFCH) dataset
+|  style="vertical-align: top;"|The Passau Spontaneous Football Coach Humour (Passau-SFCH) dataset
 |  style="vertical-align: top;"|AUC of audio modality  0.773;  AUC of video modality 0.925
 |-style="text-align:left"

@@ Line 357: / Line 357: @@
 |  style="vertical-align: top;"|CMU-MOS, Accuracy   45.0%, F1 84.3%, MAE 0.776;  CMU-MOSEI  Accuracy  86.2%, <br> F1 86.1%,  MAE  0.529,  UR-FUNNY,  Accuracy  71.68%
 |-style="text-align:left"
-|  style="vertical-align: top;"|[41]
+|  style="vertical-align: top;text-align:center;"|[41]
 |  style="vertical-align: top;"|Twitter
 |  style="vertical-align: top;"|A new CNN model
@@ Line 402: / Line 402: @@
 |  style="vertical-align: top;"|New model: Accuracy    0.897, Recall 0.903 (16000 One-Liners)
 |}
 ==5. Datasets and languages==

@@ Line 677: / Line 677: @@
 In order to enhance the precision of the model, we are used to using sophisticated models (deep network hierarchy, large number of parameters), and even selecting models that are integrated with multiple models, which leads to the need for a large number of computational resources and a huge dataset to support this "big" model. However, when deploying the service, it becomes clear that this "big" model is slow to reason and consumes a lot of memory. So distillation could be another direction to be explored for humor detection.
-=9. Conclusion=
+==9. Conclusion==
 This is the first paper reviewing humor detection to our knowledge. There are a number of significant insights from this survey. Studies on humor detection were carried on almost for English language. However, other languages, such as France and Malay, have not yet been explored and should be taken into consideration for future studies. The survey also shows that the almost works in humor detection based on deep learning use attention mechanism and multimodal technique. With the emergence of large models, models for humor detection have been newly inspired, and their recognition performance has greatly improved. But there’s very little mention of pre-processing. In addition, most of the data collected by the authors themselves come from Twitter and did not publish publicly. Use of privately generated data will result in biased performance which would make it difficult to compare new research with the benchmark research. There are many other areas of application that have yet to be explored, such as Weibo, YouTube.
@@ Line 683: / Line 683: @@
 Over the past 10 years, remarkable progress has been made in humor recognition. this article surveyed 29 papers and has addressed some significant topics required of the research landscape in humor detection. A detailed survey of the literatures have been carried out with an emphasis on humor detection, e.g., approach based deep learning, techniques (pre-processing, attention mechanism and multimodal), analysis of dataset, definition of problem, humor studies in linguistics. It also discusses, but is not limited to, some promising future directions at final section of the article.
-Acknowledgments
+==Acknowledgments==
-This work is supported by SICHUAN INTERNATIONAL STUDIES UNIVERSITY 2023 Planning Project (sisu202306)
+This work is supported by SICHUAN INTERNATIONAL STUDIES UNIVERSITY 2023 Planning Project (sisu202306).
-References
+==References==
-[1] Ramakristanaiah C., Namratha P., Ganiya R.K., Reddy M.R. A Survey on Humor Detection Methods in Communications, 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 668-674, 2021.
+[1] Ramakristanaiah C., Namratha P., Ganiya R.K., Reddy M.R. A survey on humor detection methods in communications. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), IEEE, 668-674, 2021.
-[2] Antony K., Panagiotis A. Computational Humor Recognition: A Systematic Literature Review. 2023.
+[2] Antony K., Panagiotis A. Computational humor recognition: A Systematic Literature Review.  Research Square, 2023.
-[3] Attardo S.  Humorous texts: A semantic and pragmatic analysis, Walter de Gruyter.2001.
+[3] Attardo S.  Humorous texts: A semantic and pragmatic analysis. Series Humor Research, vol. 6, Walter de Gruyter, 2001.
-[4] Attardo S. Irony as relevant inappropriateness, Journal of pragmatics, 32 (6): 793-826. 2000.
+[4] Attardo S. Irony as relevant inappropriateness. Journal of Pragmatics, 32(6):793-826. 2000.
 [5] Shahe K. Humour styles, personality, and well‐being among Lebanese university students, European journal of Personality, 18 (3): 209-219. 2004.

@@ Line 658: / Line 658: @@
 ==8. Future work==
-Building on the discussion and research presented in the previous sections, we have outlined many directions for future research and identified the unresolved issues. In figure 2，the most important future areas of research and challenges in humor detection were presented.
+Building on the discussion and research presented in the previous sections, we have outlined many directions for future research and identified the unresolved issues. In [[#img-2|Figure 2]], the most important future areas of research and challenges in humor detection were presented.
 Unexplored data domain, dataset problem, language problem, hybrid method, Research of multimodal, improving the performance of humor detection, mining useful feature for humor detection, knowledge distillation of humor detection model.
-[[Image:Draft_Ren_105609095-image2.png|492px]]
+<div id='img-2'></div>
-As can be seen from figure 2, there are still many fields in need of more detailed investigation and development. For instance, for humor detection and evaluation, there are few standard datasets available. Those datasets are small. Humor detection is aided by public standards and large datasets. Another problem that emerged from the survey carried out is that almost all of the are in English. Others languages are not yet unexplored in humor detection. One potential future trend is to hybridize several existing methods to overcome the drawbacks of each individual of each individual technique.
+As can be seen from [[#img-2|Figure 2]], there are still many fields in need of more detailed investigation and development. For instance, for humor detection and evaluation, there are few standard datasets available. Those datasets are small. Humor detection is aided by public standards and large datasets. Another problem that emerged from the survey carried out is that almost all of the are in English. Others languages are not yet unexplored in humor detection. One potential future trend is to hybridize several existing methods to overcome the drawbacks of each individual of each individual technique.
 An unsolved problem is to explore the implicit and useful feature of humor detection. The use of attentional mechanisms is now a common means of detecting significant features. However, it needs a large amount of data and high computational cost. Perhaps there are more efficient, usable, and less costly methods to be mined to solve feature extraction for humor detection.