Journal ArticleDOI
Multimodal sentimental analysis for social media applications: A comprehensive review
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TLDR
This work aims to present a survey of recent developments in analyzing the multimodal sentiments (involving text, audio, and video/image) which involve human–machine interaction and challenges involved in analyzing them.Abstract:
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 human–machine 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.read more
Citations
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Occupational stress management of college English teachers under flipped classroom teaching model
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Exploring User Requirements of Network Forensic Tools
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Journal ArticleDOI
Multimodal sentiment analysis based on fusion methods: A survey
TL;DR: The main challenge in multimodal sentiment analysis is the integration of cross-modal sentiment information, so as discussed by the authors focus on introducing the framework and characteristics of different fusion methods and discuss the development status, popular datasets, feature extraction algorithms, application areas, and existing challenges.
Journal ArticleDOI
CRNet: a multimodal deep convolutional neural network for customer revisit prediction
TL;DR: Wang et al. as mentioned in this paper proposed a multimodal deep convolutional neural network (CRNet) for predicting customer revisits, which achieved state-of-the-art performance.
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Image Steganography Performance Analysis Using Discrete Wavelet Transform and Alpha blending for Secure Communication
Saleem S Tevaramani,Joannah Ravi +1 more
TL;DR: In this paper , a Haar Discrete Wavelet Transformation (DWT) is applied to both the cover and payload images to generate a stego image, the payload image is encrypted and fused with the cover image.
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