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
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References
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Proceedings ArticleDOI
Speech Emotion Recognition Using Segmental Level Prosodic Analysis
TL;DR: Recognition performance of emotions using segmental level prosodic features is not found to be appreciable, but by combining spectral features along with Prosodic features, emotion recognition performance is considerably improved.
Proceedings ArticleDOI
Sentiment-Aspect Extraction based on Restricted Boltzmann Machines
TL;DR: A novel sentiment and aspect extraction model based on Restricted Boltzmann Machines is proposed to jointly address these two tasks in an unsupervised setting and outperforms previous state-of-the-art methods.
Proceedings ArticleDOI
Detecting irony and sarcasm in microblogs: The role of expressive signals and ensemble classifiers
TL;DR: Experimental results highlight two main findings: not all the features are equally able to characterize sarcasm and irony and BMA not only outperforms traditional state of the art models, but is also able to ensure notable generalization capabilities both on ironic and sarcastic text.
Journal ArticleDOI
On predicting elections with hybrid topic based sentiment analysis of tweets
Barkha Bansal,Sangeet Srivastava +1 more
TL;DR: This paper introduces a novel method: Hybrid Topic Based Sentiment Analysis (HTBSA) with the aim of capturing word relations and co-occurrences in short length tweets for election prediction using tweets.
Proceedings ArticleDOI
Training combination strategy of multi-stream fused hidden Markov model for audio-visual affect recognition
TL;DR: The experimental results suggest that MFHMM outperforms IHMM which assumes the independence among streams, and the training combination strategy has the superiority over the weighting combination under clean and varying audio channel noise condition.
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