Multimodal Sentiment Analysis with Multi-perspective Fusion Network Focusing on Sense Attentive Language

Li, Xia; Chen, Minping

doi:10.1007/978-3-030-63031-7_26

Cited by 15 publications

(9 citation statements)

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“…-A sense-level attention framework is designed to learn the word representation dynamically using the fusion of different methods. (Li & Chen, 2020) It achieves the most significant performance improvement.…”

Section: It Does Not Provide Better Performancementioning

confidence: 97%

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Effective deep learning based multimodal sentiment analysis from unstructured big data

2022

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More recently, as images, memes and graphics interchange formats have dominated social feeds, typographic/infographic visual content has emerged as an important social media component. This multimodal text combines text and image, defining a novel visual language that must be analysed because it has the potential to modify, confirm or grade the sentiment's polarity. The problem is how to effectively use information from the visual and textual content in image-text posts. This article presents a new deep learning-based multimodal sentiment analysis (MSA) model using multimodal data such as images, text and multimodal text (image with embedded text). The text analytic unit, the discretization control unit, the picture analytic component and the decision-making component are all included in this system. The discretization unit separates the text from the picture using the variant and channel augmented maximally stable extremal regions (VCA-MSERs) technique, which are then analysed as discrete elements and fed into the appropriate image and text analytics units. The text analytics system utilizes a stacked recurrent neural network with multilevel attention and feedback module (SRNN-MAFM) to detect the sentiment of the text. A deep convolutional neural network (CNN) structure with parallel-dilated convolution and self-attention module (PDC-SAM) is developed to forecast the emotional response to visual content. Finally, the decision component employs a Boolean framework including an OR function to evaluate and classify the output into three fine-grained sentiment classes: positive, neutral and negative. The proposed work is simulated in the python platform using the STS-Gold, Flickr 8k and B-T4SA datasets for sentiment analysis of text and visual and multimodal text. Simulation outcomes proved that the suggested method achieved better accuracy of 97.8%, 97.7% and 90% for text, visual and MSA individually compared to other methods.

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Section: It Does Not Provide Better Performancementioning

confidence: 97%

“…Multi‐perspective fusion network‐based MSA (Li & Chen, 2020) concentrates on sense attentive language. A sense‐level attention network was utilized to learn the word depiction, which is directed through a fusion of numerous modalities.…”

Section: Related Workmentioning

confidence: 99%

Effective deep learning based multimodal sentiment analysis from unstructured big data

2022

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“…A multiperspective fusion network was proposed by the researchers (X. Li & Chen, 2020) to perform multimodal sentiment analysis with the help of language modality on different datasets like CMU-MOSEI and CMU-MOSI. The main idea behind the use of language modality is that the authors believe it to have more information than visual and acoustic modalities.…”

Section: Challenges Involved In Multimodal Sentiment Analysismentioning

confidence: 99%

Multimodal sentimental analysis for social media applications: A comprehensive review

Chandrasekaran

Nguyen

Hemanth

2021

WIREs Data Min & Knowl

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The analysis of sentiments is essential in identifying and classifying opinions regarding a source material that is, a product or service. The analysis of these sentiments finds a variety of applications like product reviews, opinion polls, movie reviews on YouTube, news video analysis, and health care applications including stress and depression analysis. The traditional approach of sentiment analysis which is based on text involves the collection of large textual data and different algorithms to extract the sentiment information from it. But multimodal sentimental analysis provides methods to carry out opinion analysis based on the combination of video, audio, and text which goes a way beyond the conventional text-based sentimental analysis in understanding human behaviors. The remarkable increase in the use of social media provides a large collection of multimodal data that reflects the user's sentiment on certain aspects. This multimodal sentimental analysis approach helps in classifying the polarity (positive, negative, and neutral) of the individual sentiments. Our work aims to present a survey of recent developments in analyzing the multimodal sentiments (involving text, audio, and video/image) which involve humanmachine interaction and challenges involved in analyzing them. A detailed survey on sentimental dataset, feature extraction algorithms, data fusion methods, and efficiency of different classification techniques are presented in this work.

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“…They conducted experiments on two datasets for their research: MELD and IEMOCAP. Li et al [29] introduced a method for MSA that utilized a multi-perspective fusion network. They researched CMU-MOSI, MOSEI, and YouTube public datasets and found that their method improved accuracy by 2.9 percent.…”

Section: Literature Reviewmentioning

confidence: 99%

Urdu Sentiment Analysis via Multimodal Data Mining Based on Deep Learning Algorithms

et al. 2021

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Multimodal Sentiment Analysis with Multi-perspective Fusion Network Focusing on Sense Attentive Language

Cited by 15 publications

References 20 publications

Effective deep learning based multimodal sentiment analysis from unstructured big data

Effective deep learning based multimodal sentiment analysis from unstructured big data

Multimodal sentimental analysis for social media applications: A comprehensive review

Urdu Sentiment Analysis via Multimodal Data Mining Based on Deep Learning Algorithms

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