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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.

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References
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Journal ArticleDOI

Multimodal sentiment analysis using hierarchical fusion with context modeling

TL;DR: This article proposed a hierarchical feature fusion strategy that fuses the modalities two in two and only then fuses all three modalities in a hierarchical fashion to improve the multimodal fusion mechanism.
Journal ArticleDOI

Multimodal Sentiment Analysis of Spanish Online Videos

TL;DR: Using multimodal sentiment analysis, the presented method integrates linguistic, audio, and visual features to identify sentiment in online videos.
Journal ArticleDOI

Experiments with SVM to classify opinions in different domains

TL;DR: This paper explores this new research area applying Support Vector Machines (SVM) for testing different domains of data sets and using several weighting schemes to prove the feasibility of the SVM for different domains.
Proceedings Article

On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter

TL;DR: The results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches and the dynamic generation of stopword lists appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and shrinking the feature space.
Journal ArticleDOI

Sentiment Analysis: A Comparative Study on Different Approaches☆

TL;DR: This paper compares the various techniques used for Sentiment Analysis by analyzing various methodologies and finds several methods for accomplishing this task to be superior.
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Trending Questions (1)
How does sentiment analysis on social media influence consumer purchase patterns?

The provided paper does not specifically discuss how sentiment analysis on social media influences consumer purchase patterns.