scholarly journals Earthquake Damage Assessment Based on User Generated Data in Social Networks

2021 ◽  
Vol 13 (9) ◽  
pp. 4814
Author(s):  
Sajjad Ahadzadeh ◽  
Mohammad Reza Malek
Keyword(s):  
Social Media ◽  
Damage Assessment ◽  
Pearson Correlation ◽  
Temporal Analysis ◽  
Text Messages ◽  
Support Vector ◽  
Svm Classifier ◽  
Social Media Text ◽  
The Impact ◽  
Damage Map

Natural disasters have always been one of the threats to human societies. As a result of such crises, many people will be affected, injured, and many financial losses will incur. Large earthquakes often occur suddenly; consequently, crisis management is difficult. Quick identification of affected areas after critical events can help relief workers to provide emergency services more quickly. This paper uses social media text messages to create a damage map. A support vector machine (SVM) machine-learning method was used to identify mentions of damage among social media text messages. The damage map was created based on damage-related tweets. The results showed the SVM classifier accurately identified damage-related messages where the F-score attained 58%, precision attained 56.8%, recall attained 59.25%, and accuracy attained 71.03%. In addition, the temporal pattern of damage and non-damage tweets was investigated on each day and per hour. The results of the temporal analysis showed that most damage-related messages were sent on the day of the earthquake. The results of our research were evaluated by comparing the created damage map with official intensity maps. The findings showed that the damage of the earthquake can be estimated efficiently by our strategy at multispatial units with an overall accuracy of 69.89 at spatial grid unit and Spearman’s rho and Pearson correlation of 0.429 and 0.503, respectively, at the spatial county unit. We used two spatial units in this research to examine the impact of the spatial unit on the accuracy of damage assessment. The damage map created in this research can determine the priority of the relief workers.

scholarly journals To Enhance the Impact of Deep Learning-Based Algorithms in Determining the Behavior of an Individual based on Communication on Social Media

2019 ◽  
Vol 8 (12) ◽  
pp. 4433-4435
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Social Media Text ◽  
The Impact

In this digitized world, the Internet has become a prominent source to glean various kinds of information. In today’s scenario, people prefer virtual reality instead of one to one communication. The Majority of the population prefers social networking sites to voice themselves through posts, blogs, comments, likes, dislikes. Their sentiments can be found/traced using opinion mining or Sentiment analysis. Sentiment analysis of social media text is a useful technique for identifying peoples’ positive, negative or neutral emotions/sentiments/opinions. Sentiment analysis has gained special attention by researchers from last few years. Traditionally many machine learning algorithms were used to implement it like navie bays, Support Vector Machine and many more. But to overcome the drawbacks of ML in terms of complex classification algorithms different deep learning-based algorithms are introduced like CNN, RNN, and HNN. In this paper, we have studied different deep learning algorithms and intended to propose a deep learning-based model to analyze the behavior of an individual using social media text. Results given by the proposed model can utilize in a range of different fields like business, education, industry, politics, psychology, security, etc.

scholarly journals Sentiment Analysis of Impact of Technology on Employment from Text on Twitter

2020 ◽  
Vol 14 (07) ◽  
pp. 88
Author(s):  
Shahzad Qaiser ◽  
Nooraini Yusoff ◽  
Farzana Kabir Ahmad ◽  
Ramsha Ali
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Issues ◽  
Support Vector ◽  
Ripple Effect ◽  
Learning Classifier ◽  
The People ◽  
Impact Of Technology ◽  
Negative Sentiment ◽  
The Impact

Many different studies are in progress to analyze the content created by the users on social media due to its influence and social ripple effect. Various content created on social media has pieces of information and user’s sentiments about social issues. This study aims to analyze people’s sentiments about the impact of technology on employment and advancements in technologies and build a machine learning classifier to classify the sentiments. People are getting nervous, depressed and even doing suicides due to unemployment; hence, it is essential to explore this relatively new area of research. The study has two main objectives 1) to preprocess text collected from Twitter concerning the impact of technology on employment and analyze its sentiment, 2) to evaluate the performance of machine learning Naïve Bayes (NB) classifier on the text. To achieve this, a methodology is proposed that includes 1) data collection and preprocessing 2) analyze sentiment, 3) building machine learning classifier and 4) compare the performance of NB and support vector machine (SVM). NB and SVM achieved 87.18% and 82.05% accuracy respectively. The study found that 65% of the people hold negative sentiment regarding the impact of technology on employment and technological advancements; hence people must acquire new skills to minimize the effect of structural unemployment.

scholarly journals Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6793
Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Attique Khan ◽  
Mussarat Yasmin ◽  
Jamal Hussain Shah ◽  
Marcin Gabryel ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Data Augmentation ◽  
Feature Fusion ◽  
Pearson Correlation ◽  
Document Classification ◽  
Document Image ◽  
Support Vector ◽  
Neural Network Models ◽  
The Impact

Documents are stored in a digital form across several organizations. Printing this amount of data and placing it into folders instead of storing digitally is against the practical, economical, and ecological perspective. An efficient way of retrieving data from digitally stored documents is also required. This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes. The proposed technique’s major steps include data augmentation, feature extraction using pre-trained neural network models, feature fusion, and feature selection. We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. The DCNN features are extracted using the VGG19 and AlexNet networks. The extracted features are fused, and the fused feature vector is optimized by applying a Pearson correlation coefficient-based technique to select the optimized features while removing the redundant features. The proposed technique is tested on the Tobacco3482 dataset, which gives a classification accuracy of 93.1% using a cubic support vector machine classifier, proving the validity of the proposed technique.

scholarly journals Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis (Preprint)

2021 ◽  
Author(s):  
Siru Liu ◽  
Jili Li ◽  
Jialin Liu
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Transfer Learning ◽  
Behavioral Intentions ◽  
Temporal Analysis ◽  
Public Understanding ◽  
Support Vector ◽  
Learning Models ◽  
The Public ◽  
Over Time

BACKGROUND The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines. OBJECTIVE Our objective was to analyze the potential of leveraging transfer learning to detect tweets containing opinions, attitudes, and behavioral intentions toward COVID-19 vaccines, and to explore temporal trends as well as automatically extract topics across a large number of tweets. METHODS We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling on a dataset of COVID-19 vaccine–related tweets posted from November 1, 2020 to January 31, 2021. We used the F1 values as the primary outcome to compare the performance of machine learning and transfer learning models. The statistical values and <i>P</i> values from the Augmented Dickey-Fuller test were used to assess whether users’ perceptions changed over time. The main topics in tweets were extracted by latent Dirichlet allocation analysis. RESULTS We collected 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets. The F1 values of transfer learning models were 0.792 (95% CI 0.789-0.795), 0.578 (95% CI 0.572-0.584), and 0.614 (95% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we selected tweets in the following categories: positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions. We then identified 10 main topics and relevant terms for each category. CONCLUSIONS Overall, we provided a method to automatically analyze the public understanding of COVID-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines.

scholarly journals The Impact of Social Networking Sites on Study Habits and Interpersonal Relationships among Vietnamese Students

2021 ◽  
pp. 221-233
Author(s):  
Vu Van Tuan
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Interpersonal Relationships ◽  
Social Networking Sites ◽  
Pearson Correlation ◽  
Negative Influence ◽  
Study Habits ◽  
Quantitative Methodology ◽  
Human Beings ◽  
The Impact

Social media has a profound influence on every aspect of human beings nowadays. This study investigated the impact of social networking sites on study habits and interpersonal relationships at the tertiary level. A total of 125 college students from different universities in Hanoi were chosen through a convenience sampling technique. Quantitative methodology was employed for the research instrument and a descriptive survey design was adopted for this study. The researchers designed questionnaires with Cronbach's alpha reliability coefficients of at least 0.84 to collect data for the study. Analysis of the data was carried out using frequencies, percentages, means, t-tests, and Pearson correlation statistics at the 0.05 alpha level. The findings revealed that students’ level of using social networking sites had a negative influence on their study habits and their interpersonal relationships. Based on the findings, it was recommended that regular orientations should be given to students on how and when to use social media to enhance their study habits or to spend time improving their interpersonal relationships with their families, friends, and teachers.

scholarly journals Digital Marketing Effects of Clubhouse on Crowdfunding in the Context of COVID-19

2021 ◽  
Vol 14 (8) ◽  
pp. 347
Author(s):  
Peter Konhäusner ◽  
Robert Seidentopf
Keyword(s):  
Social Media ◽  
Research Question ◽  
Text Messages ◽  
Media Richness Theory ◽  
Expert Interview ◽  
The Social ◽  
Expansion Theory ◽  
The Media ◽  
The Impact ◽  
Media Channel

In the marketing mix, promotion is mentioned as using the communication channels available to present and market the product or service at hand. In recent years, social media has risen as an influential marketing communication channel in digital space. Apart from end-to-end direct messengers and video communication in times of the COVID-19 pandemic, the social media channel Clubhouse offers an audio-only experience. The current research lacks analysis of the potential influence of the hyped social network. Due to the novelty of the channel and the absence of text messages as well as visual stimuli, questions regarding the impact that usage of this social media channel might have on crowdfunding, a means of rising popularity in alternative financing, have arisen. The study builds upon the media richness theory of Daft and Lengel as well as the channel expansion theory of Carlson and Zmud. Besides literature research, explorative expert interview analyses were applied to answer the research question at hand. The main findings include different approaches to foster the opportunities of Clubhouse for marketing crowdfunding campaigns in line with insights about the user group of Clubhouse as well as development options for the platform.

scholarly journals Ecological Environment Changes of Mining Areas Around Nansi Lake With Remote Sensing Monitoring

2021 ◽  
Author(s):  
Hu Liu ◽  
Yan Jiang ◽  
Rafal Misa ◽  
Junhai Gao ◽  
Mingyu Xia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coal Mining ◽  
Large Scale ◽  
Underground Mining ◽  
Water Area ◽  
Ecological Environment ◽  
Support Vector ◽  
Svm Classifier ◽  
Nansi Lake ◽  
The Impact

Abstract Underground mining activity has existed for more than 100 years in Nansi lake. Coal mining not only plays a supporting role in local social and economic development but also has a significant impact on the ecological environment in the region. Landsat series remote sensing data (1988~2019) are used to research the impact of coal mining on the ecological environment in Nansi lake. Then Support Vector Machine (SVM) classifier is applied to extract the water area of the upstream lake from 1988 to 2019, and ecological environment and spatiotemporal variation characteristics are analyzed by Remote Sensing Ecology Index (RSEI). The results illustrate that the water area change is associated with annual precipitation. Compared with 2009, the ecological quality of the lake is worse in 2019, and then the reason for this change is due to large-scale underground mining. Therefore, the coal mines from the natural reserve may be closed or limited to the mining boundary for protecting the lake's ecological environment.

scholarly journals Big Data Sentimental Analysis Using Document to Vector and Optimized Support Vector Machine

2020 ◽  
Vol 4 (1) ◽  
pp. 18
Author(s):  
Sozan Abdulla Mahmood ◽  
Qani Qabil Qasim
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Human Life ◽  
Rapid Evolution ◽  
Vector Model ◽  
Support Vector ◽  
Svm Classifier ◽  
Grey Wolf Optimizer ◽  
Particle Swarm Optimizer ◽  
Short Text

With the rapid evolution of the internet, using social media networks such as Twitter, Facebook, and Tumblr, is becoming so common that they have made a great impact on every aspect of human life. Twitter is one of the most popular micro-blogging social media that allow people to share their emotions in short text about variety of topics such as company’s products, people, politics, and services. Analyzing sentiment could be possible as emotions and reviews on different topics are shared every second, which makes social media to become a useful source of information in different fields such as business, politics, applications, and services. Twitter Application Programming Interface (Twitter-API), which is an interface between developers and Twitter, allows them to search for tweets based on the desired keyword using some secret keys and tokens. In this work, Twitter-API used to download the most recent tweets about four keywords, namely, (Trump, Bitcoin, IoT, and Toyota) with a different number of tweets. “Vader” that is a lexicon rule-based method used to categorize downloaded tweets into “Positive” and “Negative” based on their polarity, then the tweets were protected in Mongo database for the next processes. After pre-processing, the hold-out technique was used to split each dataset to 80% as “training-set” and rest 20% “testing-set.” After that, a deep learning-based Document to Vector model was used for feature extraction. To perform the classification task, Radial Bias Function kernel-based support vector machine (SVM) has been used. The accuracy of (RBF-SVM) mainly depends on the value of hyperplane “Soft Margin” penalty “C” and γ “gamma” parameters. The main goal of this work is to select best values for those parameters in order to improve the accuracy of RBF-SVM classifier. The objective of this study is to show the impacts of using four meta-heuristic optimizer algorithms, namely, particle swarm optimizer (PSO), modified PSO (MPSO), grey wolf optimizer (GWO), and hybrid of PSO-GWO in improving SVM classification accuracy by selecting the best values for those parameters. To the best of our knowledge, hybrid PSO-GWO has never been used in SVM optimization. The results show that these optimizers have a significant impact on increasing SVM accuracy. The best accuracy of the model with traditional SVM was 87.885%. After optimization, the highest accuracy obtained with GWO is 91.053% while PSO, hybrid PSO-GWO, and MPSO best accuracies are 90.736%, 90.657%, and 90.557%, respectively.

scholarly journals Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

2019 ◽  
Vol 8 (9S4) ◽  
pp. 39-47
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Cuckoo Search ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Performance Factors

Sentiment analysis is an area of natural language processing (NLP) and machine learning where the text is to be categorized into predefined classes i.e. positive and negative. As the field of internet and social media, both are increasing day by day, the product of these two nowadays is having many more feedbacks from the customer than before. Text generated through social media, blogs, post, review on any product, etc. has become the bested suited cases for consumer sentiment, providing a best-suited idea for that particular product. Features are an important source for the classification task as more the features are optimized, the more accurate are results. Therefore, this research paper proposes a hybrid feature selection which is a combination of Particle swarm optimization (PSO) and cuckoo search. Due to the subjective nature of social media reviews, hybrid feature selection technique outperforms the traditional technique. The performance factors like f-measure, recall, precision, and accuracy tested on twitter dataset using Support Vector Machine (SVM) classifier and compared with convolution neural network. Experimental results of this paper on the basis of different parameters show that the proposed work outperforms the existing work

scholarly journals Application of coupled FICA-FCM feature extraction and QPSO-SVM classifier model to predict coal and gas outbursts

2020 ◽  
Author(s):  
Xuning Liu ◽  
Zixian Zhang ◽  
Zhixiang Li ◽  
Guoying Zhang
Keyword(s):  
Clustering Analysis ◽  
Coupling Model ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Svm Classifier ◽  
Analysis Model ◽  
Fast Independent Component Analysis ◽  
Sample Data ◽  
Coal And Gas Outbursts ◽  
The Impact

Abstract The coal and gas outbursts samples data are affected by all kinds of influencing factors, the accuracy of classification on these sample data is not high, the classification of some samples always have errors, which may be inaccurate annotation. In order to reduce the impact of noise data caused by the labeling errors on classification, this paper proposes a combination of classifier and clustering analysis model, which is used to improve the prediction accuracy of coal and gas outbursts. First, the high-order statistical characteristics of fast independent component analysis(FICA) method are used to extract the features of coal and gas outbursts sample data to obtain the independent nonlinear main metadata; Then, the fuzzy clustering mean(FCM) algorithm is used to cluster the coal and gas outbursts samples. Finally, based on improved clustering analysis results, the support vector machine(SVM) classifier based on quantum particle swarm optimization(QPSO) algorithm is used to compare the classification results with the existing labeled results, and the classification results are corrected to improve the classification accuracy. The results show that the performance of coupling model is significantly better than other exiting classifiers for coal and gas outbursts prediction.

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