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

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.

scholarly journals Implementation Analysis of Data Classification Approach for Sentiment Classification

2021 ◽  
Vol 9 (VII) ◽  
pp. 1509-1512
Author(s):  
Bhushan R. Chincholkar
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Analysis ◽  
Hate Speech ◽  
Modern Technology ◽  
Quality Information ◽  
Classification Approach ◽  
Potential Benefits ◽  
Better Than

Sentiment analysis is one of the fastest growing fields with its demand and potential benefits that are increasing every day. Sentiment analysis aims to classify the polarity of a document through natural language processing, text analysis. With the help of internet and modern technology, there has bee n a tremendous growth in the amount of data. Each individual is in position to precise his/her own ideas freely on social media. All of this data can be analyzed and used in order to draw benefits and quality information. In this paper, the focus is on cyber-hate classification based on for public opinion or views, since the spread of hate speech using social media can have disruptive impacts on social sentiment analysis. In particular, here proposing a modified approach with two stage training for dealing with text ambiguity and classifying three type approach positive, negative and neutral sentiment, and compare its performance with those popular methods also as well as some existing fuzzy approaches. Afterword comparing the performance of proposed approach with commonly used sentiment classifiers which are known to perform well in this task. The experimental results indicate that our modified approach performs marginally better than the other algorithms.

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

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.

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

2020 ◽  
pp. 939-956
Author(s):  
Youjia Fang ◽  
Xin Chen ◽  
Zheng Song ◽  
Tianzi Wang ◽  
Yang Cao
Keyword(s):  
Social Media ◽  
Big Data ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Information Diffusion ◽  
Model Fitting ◽  
Compartmental Models ◽  
Public Opinions ◽  
Estimation And Prediction

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.

Detection of Cyberbullying on Social Media Using Machine learning

2021 ◽  
Vol 9 (10) ◽  
pp. 1401-1409
Author(s):  
Mitta Roja
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Extraction ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hate Speech ◽  
Text Data ◽  
Model Based

Abstract: Cyberbullying is a major problem encountered on internet that affects teenagers and also adults. It has lead to mishappenings like suicide and depression. Regulation of content on Social media platorms has become a growing need. The following study uses data from two different forms of cyberbullying, hate speech tweets from Twittter and comments based on personal attacks from Wikipedia forums to build a model based on detection of Cyberbullying in text data using Natural Language Processing and Machine learning. Threemethods for Feature extraction and four classifiers are studied to outline the best approach. For Tweet data the model provides accuracies above 90% and for Wikipedia data it givesaccuracies above 80%. Keywords: Cyberbullying, Hate speech, Personal attacks,Machine learning, Feature extraction, Twitter, Wikipedia

Coronavirus Pandemic (COVID-19)

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.

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

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.

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

2020 ◽  
pp. 879-896
Author(s):  
Youjia Fang ◽  
Xin Chen ◽  
Zheng Song ◽  
Tianzi Wang ◽  
Yang Cao
Keyword(s):  
Social Media ◽  
Big Data ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Information Diffusion ◽  
Model Fitting ◽  
Compartmental Models ◽  
Public Opinions ◽  
Estimation And Prediction

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.

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

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.

scholarly journals Occupants’ Satisfaction with LEED- and Non-LEED-Certified Apartments Using Social Media Data

2021 ◽  
Author(s):  
Xingtong Guo ◽  
Kyumin Lee ◽  
Zhe Wang ◽  
Shichao Liu
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Topic Modeling ◽  
System Development ◽  
Residential Buildings ◽  
Statistical Significance ◽  
Environmental Design ◽  
Online Reviews ◽  
Star Rating

Leadership in Energy and Environmental Design (LEED) certified buildings aim to offer a sustainable and healthy built environment. Previous studies have shown mixed and inconsistent results on whether occupants in LEED-certified buildings are more satisfying than in non-LEED-certified counterparts. Those studies usually based on surveys or questionnaires for commercial buildings were limited by sample size and pre-defined question structures. Since most people stay longer at home during the COVID-19 pandemic and the trend might continue in the post-pandemic era, assessing the satisfaction with LEED-certified residential buildings benefits future environmental design and certification system development. In this work, we propose a natural language processing-based approach for such assessment. The study collected 16,761 online reviews on 260 LEED-certified apartments and 180 non-LEED-certified-apartments from social media, then applied topic modeling and sentiment analysis to evaluate occupants’ satisfaction. Based on topic modeling, we categorized online comments into three topic clusters, 1) location and transportation, 2) running cost, and 3) health and wellbeing. The subsequent sentiment analysis has shown a statistically significant but small or negligible enhancement in the satisfaction occurring in LEED-certified apartments compared to non-LEED-certified ones concerning all the three topic clusters. The “significant but small or negligible uptick” has also been found in online star rating and indoor environmental satisfaction. The only exception with a large effect size is lighting that is significantly more satisfying in LEED-certified apartments. Nevertheless, the statistical significance in online star rating disappears when it is normalized by rent price and property house value.

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