Modelling Propagation of Public Opinions on Microblogging Big Data Using Sentiment Analysis and Compartmental Models

Compartmental models have been used to model information diffusion on social media. However, there have been few studies on modelling positive and negative public opinions using compartmental models. This study aimed for using sentiment analysis and compartmental model to model the propagation of positive and negative opinions on microblogging big media. The authors studied the news propagation of seven popular social topics on China's Sina Weibo microblogging platform. Natural language processing and sentiment analysis were used to identify public opinions from microblogging big data. Then two existing (SIZ and SEIZ) models and a newly developed (SE2IZ) model were implemented to model the news propagation and evaluate the trends of public opinions on selected social topics. Simulation study was used to check model fitting performance. The results show that the new SE2IZ model has a better model fitting performance than existing models. This study sheds some new light on using social media for public opinion estimation and prediction.

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Modelling Propagation of Public Opinions on Microblogging Big Data Using Sentiment Analysis and Compartmental Models

International Journal on Semantic Web and Information Systems ◽

10.4018/ijswis.2017010102 ◽

2017 ◽

Vol 13 (1) ◽

pp. 11-27 ◽

Cited By ~ 8

Author(s):

Youjia Fang ◽

Xin Chen ◽

Zheng Song ◽

Tianzi Wang ◽

Yang Cao

Keyword(s):

Social Media ◽

Big Data ◽

Sentiment Analysis ◽

Language Processing ◽

Information Diffusion ◽

Compartmental Model ◽

Model Fitting ◽

Compartmental Models ◽

Sina Weibo ◽

Public Opinions

Compartmental models have been used to model information diffusion on social media. However, there have been few studies on modelling positive and negative public opinions using compartmental models. This study aimed for using sentiment analysis and compartmental model to model the propagation of positive and negative opinions on microblogging big media. The authors studied the news propagation of seven popular social topics on China's Sina Weibo microblogging platform. Natural language processing and sentiment analysis were used to identify public opinions from microblogging big data. Then two existing (SIZ and SEIZ) models and a newly developed (SE2IZ) model were implemented to model the news propagation and evaluate the trends of public opinions on selected social topics. Simulation study was used to check model fitting performance. The results show that the new SE2IZ model has a better model fitting performance than existing models. This study sheds some new light on using social media for public opinion estimation and prediction.

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Análise de discursos em notícias sobre homofobia, racismo e sexismo em comentários de portais brasileiros de notícias

10.14210/cotb.v12.p467-474 ◽

2021 ◽

Author(s):

Lucas Rodrigues ◽

Antonio Jacob Junior ◽

Fábio Lobato

Keyword(s):

Social Media ◽

Natural Language Processing ◽

Sentiment Analysis ◽

Data Visualization ◽

Language Processing ◽

Topic Modeling ◽

Hate Speech ◽

Psychological Impact ◽

Internet Service ◽

General Law

Posts with defamatory content or hate speech are constantly foundon social media. The results for readers are numerous, not restrictedonly to the psychological impact, but also to the growth of thissocial phenomenon. With the General Law on the Protection ofPersonal Data and the Marco Civil da Internet, service providersbecame responsible for the content in their platforms. Consideringthe importance of this issue, this paper aims to analyze the contentpublished (news and comments) on the G1 News Portal with techniquesbased on data visualization and Natural Language Processing,such as sentiment analysis and topic modeling. The results showthat even with most of the comments being neutral or negative andclassified or not as hate speech, the majority of them were acceptedby the users.

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Sentiment Analysis Application and Natural Language Processing for Mobile Network Operators’ Support on Social Media

2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) ◽

10.1109/icabcd.2019.8851052 ◽

2019 ◽

Author(s):

Kingsley A. Ogudo ◽

Dahj Muwawa Jean Nestor

Keyword(s):

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Mobile Network ◽

Analysis Application ◽

Mobile Network Operators

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Sentiment Analysis on Social Media Big Data With Multiple Tweet Words

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9684.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 3429-3434 ◽

Cited By ~ 2

Keyword(s):

Machine Learning ◽

Social Media ◽

Big Data ◽

Sentiment Analysis ◽

Language Processing ◽

Sentiment Classification ◽

Support Vector ◽

Decision Tree Classifier ◽

Machine Learning Classification ◽

Tree Classifier

The main objective of this paper is Analyze the reviews of Social Media Big Data of E-Commerce product’s. And provides helpful result to online shopping customers about the product quality and also provides helpful decision making idea to the business about the customer’s mostly liking and buying products. This covers all features or opinion words, like capitalized words, sequence of repeated letters, emoji, slang words, exclamatory words, intensifiers, modifiers, conjunction words and negation words etc available in tweets. The existing work has considered only two or three features to perform Sentiment Analysis with the machine learning technique Natural Language Processing (NLP). In this proposed work familiar Machine Learning classification models namely Multinomial Naïve Bayes, Support Vector Machine, Decision Tree Classifier, and, Random Forest Classifier are used for sentiment classification. The sentiment classification is used as a decision support system for the customers and also for the business.

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An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

10.1101/2020.07.30.20165571 ◽

2020 ◽

Author(s):

Sohini Sengupta ◽

Sareeta Mugde ◽

Garima Sharma

Keyword(s):

Mental Health ◽

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Gold Mine ◽

Social Media Platforms ◽

Processing Techniques

Twitter is one of the world's biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users.

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Coronavirus Pandemic (COVID-19)

International Journal on Semantic Web and Information Systems ◽

10.4018/ijswis.2021040101 ◽

2021 ◽

Vol 17 (2) ◽

pp. 1-21

Author(s):

Jalal S. Alowibdi ◽

Abdulrahman A. Alshdadi ◽

Ali Daud ◽

Mohamed M. Dessouky ◽

Essa Ali Alhazmi

Keyword(s):

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Experimental Results ◽

Mixed Emotions ◽

Social Media Platforms

People are afraid about COVID-19 and are actively talking about it on social media platforms such as Twitter. People are showing their emotions openly in their tweets on Twitter. It's very important to perform sentiment analysis on these tweets for finding COVID-19's impact on people's lives. Natural language processing, textual processing, computational linguists, and biometrics are applied to perform sentiment analysis to identify and extract the emotions. In this work, sentiment analysis is carried out on a large Twitter dataset of English tweets. Ten emotional themes are investigated. Experimental results show that COVID-19 has spread fear/anxiety, gratitude, happiness and hope, and other mixed emotions among people for different reasons. Specifically, it is observed that positive news from top officials like Trump of chloroquine as cure to COVID-19 has suddenly lowered fear in sentiment, and happiness, gratitude, and hope started to rise. But, once FDA said, chloroquine is not effective cure, fear again started to rise.

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Sentiment Analysis for Depression Based on Social Media Post by Using Natural Language Processing

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-2319 ◽

2021 ◽

pp. 134-139

Author(s):

G. Neelavathi ◽

D. Sowmiya ◽

C. Sharmila ◽

J. Vaishnavi

Keyword(s):

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Rich Source ◽

Research Center ◽

Depression Symptoms ◽

Linguistic Markers

Presently Research Center expresses that, 72% of public uses some sort of social media. More than 300 million individual experiences the depression and despondency, just a small amount of them get sufficient treatment. Discouragement is the main source of incapacity worldwide and almost 800,000 individuals consistently loss their life because of suicide. Suicide is the subsequent driving reason for death among teenagers. Our idea is to suggest solution for this problem. Social Media gives an extraordinary chance to change early depressions, especially in youngsters. Consistently, around 6,000 Tweets are tweeted per second, 350,000 tweets per minute, 500 million tweets each day and around 200 billion tweets each year. By using this rich source of data and information, can efficient model which provides report of person’s depression symptoms will be designed. In this model an algorithm that can examine Tweets Expressing self-assessed negative features by analyzing linguistic markers in social media posts.

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Tasks, Approaches, and Avenues of Opinion Mining, Sentiment Analysis, and Emotion Analysis

Advances in Social Networking and Online Communities - E-Collaboration Technologies and Strategies for Competitive Advantage Amid Challenging Times ◽

10.4018/978-1-7998-7764-6.ch006 ◽

2021 ◽

pp. 171-209

Author(s):

Amira M. Idrees ◽

Fatma Gamal Eldin ◽

Amr Mansour Mohsen ◽

Hesham Ahmed Hassan

Keyword(s):

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Potential Benefit ◽

Opinion Mining ◽

Product Reviews ◽

Huge Amount ◽

Know How

Every successful business aims to know how customers feel about its brands, services, and products. People freely express their views, ideas, sentiments, and opinions on social media for their day-to-day activities, for product reviews, for surveys, and even for their public opinions. This process provides a fortune of valuable resources about the market for any type of business. Unfortunately, it's impossible to manually analyze this massive quantity of information. Sentiment analysis (SA) and opinion mining (OM), as new fields of natural language processing, have the potential benefit of analyzing such a huge amount of data. SA or OM is the computational treatment of opinions, sentiments, and subjectivity of text. This chapter introduces the reader to a survey of different text SA and OM proposed techniques and approaches. The authors discuss in detail various approaches to perform a computational treatment for sentiments and opinions with their strengths and drawbacks.

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