An image-text consistency driven multimodal sentiment analysis approach for social media

2019 ◽  
Vol 56 (6) ◽  
pp. 102097 ◽  
Author(s):  
Ziyuan Zhao ◽  
Huiying Zhu ◽  
Zehao Xue ◽  
Zhao Liu ◽  
Jing Tian ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Approach ◽  
Multimodal Sentiment Analysis

Deep Learning Techniques for Polarity Classification in Multimodal Sentiment Analysis

2018 ◽  
Vol 17 (03) ◽  
pp. 883-910 ◽  
Author(s):  
P. D. Mahendhiran ◽  
S. Kannimuthu
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Relevant Information ◽  
Neural Net ◽  
Video Information ◽  
Automatic Feature Extraction ◽  
Multimodal Sentiment Analysis

Contemporary research in Multimodal Sentiment Analysis (MSA) using deep learning is becoming popular in Natural Language Processing. Enormous amount of data are obtainable from social media such as Facebook, WhatsApp, YouTube, Twitter and microblogs every day. In order to deal with these large multimodal data, it is difficult to identify the relevant information from social media websites. Hence, there is a need to improve an intellectual MSA. Here, Deep Learning is used to improve the understanding and performance of MSA better. Deep Learning delivers automatic feature extraction and supports to achieve the best performance to enhance the combined model that integrates Linguistic, Acoustic and Video information extraction method. This paper focuses on the various techniques used for classifying the given portion of natural language text, audio and video according to the thoughts, feelings or opinions expressed in it, i.e., whether the general attitude is Neutral, Positive or Negative. From the results, it is perceived that Deep Learning classification algorithm gives better results compared to other machine learning classifiers such as KNN, Naive Bayes, Random Forest, Random Tree and Neural Net model. The proposed MSA in deep learning is to identify sentiment in web videos which conduct the poof-of-concept experiments that proved, in preliminary experiments using the ICT-YouTube dataset, our proposed multimodal system achieves an accuracy of 96.07%.

scholarly journals Sentiment Analysis of Facebook Posts using Hybrid Method

2019 ◽  
Vol 8 (2) ◽  
pp. 2421-2428
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Hybrid Method ◽  
Analysis Approach ◽  
Hybrid Analysis ◽  
Lexical Analysis ◽  
Machine Learning Classification ◽  
Language Discourse ◽  
Popular Medium

Social Media is a popular medium of communication amongst youngsters to remain connected with their friends. Facebook is one of the most preferred Social Media Sites which store the gigantic amount of data which can be explored for Sentiment Analysis. In this study, we have applied hybrid analysis approach which combines the best features of a lexical analysis and SVM machine learning classification algorithm on Facebook Posts. The analysis is further improved by incorporating language discourse features to detect intensity of sentiment and the prominent emotions expressed through these posts.

An Advance Approach toward Sentiment Analysis using Swarm Intelligence

2021 ◽  
Vol 9 (VI) ◽  
pp. 1710-1713
Author(s):  
Miss. Riddhi Mandal
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Sentiment Analysis ◽  
Swarm Intelligence ◽  
Crucial Role ◽  
Complex Problem ◽  
Analysis Approach ◽  
Customer Feedback ◽  
The World

Modernization is the key feature for the development of Society. With the timespan people are making growth with trends in technology. Around the decades, there were many technologies which have been stepped up over the industry and made the transformation in the society and have made tremendous development throughout the world. Similarly, In the 21st decades Social media (like Facebook, Twitter, what’s app, Instagram & many more) have become one of the emphasized network mediums. Millions of people are using social media to get in touch with people staying far away from them. There are millions of data over it which is non-hierarchical and need to store and use it for feedback and other usage. Not only in Social Media, in the business & marketing sector too, customer feedback plays a crucial role. For maintaining and segregating data in a systematic way, sentiment analysis is being used which makes the task easier and helps to understand the data in a better way. In this paper, we are presenting a sentiment analysis approach using Swarm Intelligence, which could be more beneficial in such tasks to solve the complex problem. The concept is correlated with technology Artificial Intelligence.

scholarly journals Sentiment Analysis of Social Media via Multimodal Feature Fusion

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2010
Author(s):  
Kang Zhang ◽  
Yushui Geng ◽  
Jing Zhao ◽  
Jianxin Liu ◽  
Wenxiao Li
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Feature Fusion ◽  
Image Features ◽  
Research Field ◽  
Sentiment Classification ◽  
Analysis Model ◽  
Noise Interference ◽  
Proposed Model ◽  
Multimodal Sentiment Analysis

In recent years, with the popularity of social media, users are increasingly keen to express their feelings and opinions in the form of pictures and text, which makes multimodal data with text and pictures the con tent type with the most growth. Most of the information posted by users on social media has obvious sentimental aspects, and multimodal sentiment analysis has become an important research field. Previous studies on multimodal sentiment analysis have primarily focused on extracting text and image features separately and then combining them for sentiment classification. These studies often ignore the interaction between text and images. Therefore, this paper proposes a new multimodal sentiment analysis model. The model first eliminates noise interference in textual data and extracts more important image features. Then, in the feature-fusion part based on the attention mechanism, the text and images learn the internal features from each other through symmetry. Then the fusion features are applied to sentiment classification tasks. The experimental results on two common multimodal sentiment datasets demonstrate the effectiveness of the proposed model.

How significant are users’ opinions in social media?

2014 ◽  
Vol 22 (4) ◽  
pp. 254-272 ◽  
Author(s):  
Hamid Khobzi ◽  
Babak Teimourpour
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Design Methodology ◽  
Regression Method ◽  
Analysis Approach ◽  
Content Type ◽  
Polarity Score ◽  
Facebook Pages ◽  
The Relationship

Purpose – The purpose of this study is to assign polarity score to each post from Facebook fan pages, and then examine whether the Comments submitted by users on a post from fan page have a significant relationship with the popularity of that post. Being aware of how to enhance the popularity of posts will help companies in terms of administrating their fan pages. Design/methodology/approach – In the context of fan page and post popularity, the authors test significance of the relationship between Comments’ polarity and number of Likes and Comments of a post in different Facebook pages by regression method. The data are collected from different fan page posts in Facebook, and a sentiment analysis approach is proposed to accomplish this research. Findings – Results show that the relation between users’ Comments and popularity of fan page posts is strongly significant. Outcomes of this research are useful for every company in terms of monitoring and managing their brand fan pages on social networking sites such as Facebook. Originality/value – Investigation of factors influencing popularity of fan page posts in social media is almost a new area of study that dates back to recent years. The authors use a sentiment analysis approach to evaluate a new concept describing the relationship between users’ Comments and popularity of posts from Facebook fan pages. Moreover, a part of dataset is extracted from Facebook by a crawler which is an advantage to prior studies.

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