Journal ArticleDOI
Multimodal sentimental analysis for social media applications: A comprehensive review
Reads0
Chats0
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
More filters
Journal ArticleDOI
Plant Leaf Disease Detection using Computer Vision and Machine Learning Algorithms
TL;DR: In this article , the authors proposed a method to detect the leaf diseases in the tomato plant using support vector machine (SVM), convolutional neural network (CNN), and K-Nearest Neighbor (K-NN).
Journal ArticleDOI
Image classification and prediction using transfer learning in colab notebook
TL;DR: The result shows that MobileNetV2 performance is relatively better than other pre trained models and in the future enhancement transfer learning may be used for natural language processing to obtain highest accuracy.
Journal ArticleDOI
State of the art: a review of sentiment analysis based on sequential transfer learning
Journal ArticleDOI
Social media data analytics for business decision making system to competitive analysis
TL;DR: In this paper, a business decision making system (BDMS) is proposed to develop business using social media data analytics, which provides a clear understanding of the key principles, issues and functionality, and big social data developments.
Journal ArticleDOI
Big data-assisted social media analytics for business model for business decision making system competitive analysis
TL;DR: In this article, a Big Data-assisted Social Media Analytics for Business (BD-SMAB) model is proposed to increase awareness and affect decision-makers in marketing strategies.
References
More filters
Proceedings ArticleDOI
Detecting Sarcasm in Multimodal Social Platforms
TL;DR: This work first studies the relationship between the textual and visual aspects in multimodal posts from three major social media platforms, and runs a crowdsourcing task to quantify the extent to which images are perceived as necessary by human annotators.
Journal ArticleDOI
Error Weighted Semi-Coupled Hidden Markov Model for Audio-Visual Emotion Recognition
TL;DR: Experimental results show that the proposed approach not only outperforms other fusion-based bimodal emotion recognition methods for posed expressions but also provides satisfactory results for naturalistic expressions.
Journal ArticleDOI
Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data
TL;DR: The first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories addressed in review sentences.
Journal ArticleDOI
Sentiment Analysis on Twitter Data using KNN and SVM
TL;DR: This work proposes techniques to classify the sentiment label accurately and introduces two methods: one of the methods is known as sentiment classification algorithm (SCA) based on k-nearest neighbor (KNN) and the other one is based on support vector machine (SVM).
Proceedings ArticleDOI
The Cambridge University March 2005 speaker diarisation system.
TL;DR: This system combines techniques used successfully in previous speaker diarisation systems with an additional second clustering stage based on state-of-the-art speaker identification methods to give a diarification error rate of 6.9% on the RT-04 Fall darisation evaluation data.
Related Papers (5)
Sentiment Analysis of Social Media via Multimodal Feature Fusion
Deep Learning Techniques for Polarity Classification in Multimodal Sentiment Analysis
P. D. Mahendhiran,S. Kannimuthu +1 more