Table Ii From A Deep Learning Based Approach For Multimodal Sarcasm Detection Semantic Scholar

By switzerlandersing On Sep 14, 2025

Multimodal Deep Learning Models | PDF

Multimodal Deep Learning Models | PDF This paper proposes an effective method based on deep learning that utilizes both textual and visual information for multi modal sarcasm detection based on the recurrent neural network that aims to exploit the interaction among the input modalities for the prediction. Sarcasm detection is used to single out natural language statements where intended meaning differs from what the surface meaning implies. a number of tasks in n.

Multimodal Learning | PDF | Deep Learning | Attention

Multimodal Learning | PDF | Deep Learning | Attention In this approach, deep learning based on three pretrained word embedding model was used in the text model, and its effects were observed. the comparison for these cases are being shown in table 2. Here we broadly classify these approaches into three classes: (1) traditional deep learn ing models that use separate encoders for image and text, (2) multimodal transformers, and (3) llm based approaches with prompt engineering. This study proposed an innovative multimodal sarcasm detection framework called mdsan, which conducts an in depth analysis from multiple levels and perspectives, including emotion, syntax, and feature alignment, to enhance the precision and robustness of sarcasm detection. In this paper, we present the first comprehensive survey on multimodal sarcasm detection henceforth msd to date. we survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. we also present future research directions in msd.

Figure 1 From Multimodal Sarcasm Detection Based On Multimodal Sentiment Co-training | Semantic ...

Figure 1 From Multimodal Sarcasm Detection Based On Multimodal Sentiment Co-training | Semantic ... This study proposed an innovative multimodal sarcasm detection framework called mdsan, which conducts an in depth analysis from multiple levels and perspectives, including emotion, syntax, and feature alignment, to enhance the precision and robustness of sarcasm detection. In this paper, we present the first comprehensive survey on multimodal sarcasm detection henceforth msd to date. we survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. we also present future research directions in msd. This article proposed a novel approach by combining textual and audio features together to detecting sarcasm in conversational data. A model based on a supervised machine learning algorithm called support vector machine (svm) has been used and the performance of the proposed model has been compared with other models submitted to sentiment analysis and sarcasm detection shared task. Most of the existing work has been focused on either detecting sarcasm in textual data using text features or audio data using audio features. this article proposed a novel approach by combining textual and audio features together to detecting sarcasm in conversational data. This paper presents a novel multi modal sarcasm detection model leveraging cue learning techniques to address the challenges posed by data scarcity, especially in low resource languages.

Figure 1 From Multimodal Sarcasm Detection Based On Multimodal Sentiment Co-training | Semantic ...

Figure 1 From Multimodal Sarcasm Detection Based On Multimodal Sentiment Co-training | Semantic ... This article proposed a novel approach by combining textual and audio features together to detecting sarcasm in conversational data. A model based on a supervised machine learning algorithm called support vector machine (svm) has been used and the performance of the proposed model has been compared with other models submitted to sentiment analysis and sarcasm detection shared task. Most of the existing work has been focused on either detecting sarcasm in textual data using text features or audio data using audio features. this article proposed a novel approach by combining textual and audio features together to detecting sarcasm in conversational data. This paper presents a novel multi modal sarcasm detection model leveraging cue learning techniques to address the challenges posed by data scarcity, especially in low resource languages.

Table IV From Multimodal Sarcasm Detection Based On Multimodal Sentiment Co-training | Semantic ...

Table IV From Multimodal Sarcasm Detection Based On Multimodal Sentiment Co-training | Semantic ... Most of the existing work has been focused on either detecting sarcasm in textual data using text features or audio data using audio features. this article proposed a novel approach by combining textual and audio features together to detecting sarcasm in conversational data. This paper presents a novel multi modal sarcasm detection model leveraging cue learning techniques to address the challenges posed by data scarcity, especially in low resource languages.