Conversations around mHealth applications during COVID-19: a network and sentiment analysis of Tweets in Saudi Arabia (Preprint)

2021 ◽  
Author(s):  
Samar Binkheder ◽  
Raniah N Aldekhyyel ◽  
Alanoud Almegbil ◽  
Nora Al-Twairesh ◽  
Nuha Alhumaid ◽  
...  
Keyword(s):  
Social Media ◽  
Saudi Arabia ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Data Augmentation ◽  
Support Vector ◽  
Learning Approaches ◽  
The Government ◽  
The Impact ◽  
Mhealth Apps

BACKGROUND In Saudi Arabia, the first novel coronavirus disease (COVID-19) confirmed case was reported on March 2, 2020, which followed a series of mitigation efforts imposed by the government. The development of specific mobile health applications (mHealth apps) for public use was one of the response strategies employed by the Saudi government. Assessing the impact of these mHealth apps through the opinions of the public posted on social media is crucial to improve mHealth services offered by governments. OBJECTIVE Our aim was to utilize Twitter, as a source of data, to understand conversations and perceptions of users around the use of six mHealth apps developed by the Saudi Ministry of Health, by conducting a network and sentimental analysis of Tweets. The mHealth apps included in our study were “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. METHODS We collected mHealth-related Twitter data on December 12, 2020. After including relevant tweets, our final mHealth app networks consisted of a total of 4,995 Twitter users and 8,666 relationships. We used NodeXL to perform the network analysis and visualization. We performed a sentiment analysis using a human-in-the-loop and machine learning approaches. Our manual annotation initially included five classes (positive, neutral, negative, indeterminate, and sarcasm). We excluded indeterminate and sarcasm classes as they usually cause ambiguity for the sentiment classifier. We applied data augmentation techniques to ensure sentiment polarity (positive, negative) in the tweets. The sentiment classifier dataset consisted a total of 4,719 tweets with 26.6% positive, 52.2% neutral, and 21.2% negative. Data preprocessing and normalization were also performed. For building the sentiment classifier, we used the Support Vector Machine with the word2vec embeddings of AraVec. RESULTS Our network analysis showed that “Sehhaty”, “Tawakkalna”, and “Tabaud” had similar patterns and more interactions in conversations than other networks. “Tawakkalna” and “Tabaud” were the largest networks among all, and their conversations were led by various governmental accounts. In comparison, “Sehha”, “Mawid”, “Sehhaty”, and “Tetamman” networks were mainly led by a health sector and media. Our sentiment analysis showed that the majority of Twitter conversations around the six mHealth apps were neutral, which encompassed facts or information pieces, neutral suggestions, and general inquires. Positive tweets focused on appreciation, positive opinions, and expressions around government trust. In contrast, negative tweets included suggestions to overcome weaknesses, issues faced with apps, negative opinions, and negative psychological impact. Our sentiment classifier showed an accuracy, precision, recall, and an F1-score of 85%. CONCLUSIONS Social media can be used as a data source to understand public perceptions on the use of mHealth apps during pandemics. Real-time analytics of social media can help health authorities to address issues and concerns about mHealth apps during public health crises.

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 Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

2021 ◽  
Vol 18 (24) ◽  
pp. 13388
Author(s):  
Samar Binkheder ◽  
Raniah N. Aldekhyyel ◽  
Alanoud AlMogbel ◽  
Nora Al-Twairesh ◽  
Nuha Alhumaid ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Sentiment Analysis ◽  
Health Sector ◽  
Public Perceptions ◽  
Support Vector ◽  
The Public ◽  
Mobile Health Applications ◽  
Twitter Users ◽  
Mhealth Apps ◽  
Health Applications

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.

Sentiment Analysis of the Indonesian Health Ministry Performance in Covid-19 Crisis using Support Vector Machine (SVM)

2021 ◽  
Vol 10 (1) ◽  
pp. 65
Author(s):  
Fathiyarizq Mahendra Putra ◽  
I Wayan Santiyasa
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Virus Disease ◽  
Recall Rate ◽  
Training Data ◽  
Support Vector ◽  
Communication Tool ◽  
Ministry Of Health ◽  
Internet Users ◽  
The Government

Corona Virus Disease or COVID 19 is a new virus disease that originated in 2019 [6], Indonesia has reported first COVID-19 In 2nd March 2020. Various attempts have been made by the government, such as taking strict measures by temporal lockdown or cordoning off the areas that were suspected of having risks of community spread. As a source of information, the internet has changed substantially,. for example, social media. social media is a communication tool that is very popular among internet users today, From social media, users can update status, send messages, even, become a platform for exchanging socio-economic opinions and political views both in their place of residence or their country. This paper deals with the sentiment analysis of Indonesian after the peformance of Indonesian Ministry Of Health. We used the social media platform Twitter for our analysis. Tweets were studied to gauge the opinion of Indonesian towards peformance of Indonesian Ministry Of Health. Tweets were extracted using the two prominent keywords used namely: “terawan ”and “menkes” from June 15th to September 19th 2020. A total of 200 tweets were considered for the analysis. This study has successfully implemented the SVM algorithm for sentiment analysis on tweet data about peformance of Indonesian Ministry Of Health during COVID-19 Crisis. This is shown by the accuracy of using tweet data as much as 200 data, which is 172 data are training data and 28 are testing data. Besides the amount of data that affects accuracy, there are also other factors, namely the use of the kernel and the number of classes used. The results show that the Linear Kernel has the best accuracy, precision and recall rate compared to other kernels, respectively 75% for accuracy, 78.4% for precision and a recall value of 75%. for polynomial kernels, Gaussian and Sigmoid have the same accuracy, precision, and recall rates, namely, respectively. 60.71% for accuracy, 36.86% for precision and 60.71% recall value.

scholarly journals Emergent use of Social Media on Elections: the use of Data Mining and Social Network Analysis for Political Purposes

2021 ◽  
Vol 10 (6) ◽  
pp. 118-122
Author(s):  
Acharoui Zakia ◽  
Ettaki Badia ◽  
Zerouaoui Jamal
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Support Vector ◽  
Use Of Data ◽  
Election Results ◽  
Use Of Social Media ◽  
The Impact

People spend more time on social media either for personal or social interest which generates an expanding amount of Data. This paper is written for researchers seeking to have an overview of the different technical methods used for political purposes principally Data Mining and Social Network Analysis. Hence, the first part introduces the impact of Social Media on politics for different aims such as communicating with voters, promoting participation, and predicting election results, then the two main methods to achieve political purposes were presented. Data mining approaches is likely to be used on political context to classify citizen’s opinion or predicting results thus by using methods such as term occurrence, mentions, Support Vector Machine, Machine Learning, and Artificial Neural Networks. The Social Network Analysis approaches are used to retrieve data about influencers, their role during a period, and the nature of the information shared.

scholarly journals Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 374 ◽  
Author(s):  
Sudhanshu Kumar ◽  
Monika Gahalawat ◽  
Partha Pratim Roy ◽  
Debi Prosad Dogra ◽  
Byung-Gyu Kim
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Age Groups ◽  
Modern World ◽  
Support Vector ◽  
Digital Information ◽  
Age And Gender ◽  
And Gender ◽  
The Impact

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

A sentiment analysis system for social media using machine learning techniques: Social enablement

2018 ◽  
Vol 34 (3) ◽  
pp. 569-581 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Tool ◽  
Data Set ◽  
Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

scholarly journals Analisis Sentimen Data Twitter Tentang Pasangan Capres-Cawapres Pemilu 2019 Dengan Metode Lexicon Based Dan Support Vector Machine

2019 ◽  
Vol 11 (2) ◽  
pp. 144
Author(s):  
Danar Wido Seno ◽  
Arief Wibowo
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Vice President ◽  
Training Data ◽  
Support Vector ◽  
New Words ◽  
Textual Data ◽  
Data Content ◽  
Combination Of Methods

Social media writing content growing make a lot of new words that appear on Twitter in the form of words and abbreviations that appear so that sentiment analysis is increasingly difficult to get high accuracy of textual data on Twitter social media. In this study, the authors conducted research on sentiment analysis of the pairs of candidates for President and Vice President of Indonesia in the 2019 Elections. To obtain higher accuracy results and accommodate the problem of textual data development on Twitter, the authors conducted a combination of methods to conduct the sentiment analysis with unsupervised and supervised methods. namely Lexicon Based. This study used Twitter data in October 2018 using the search keywords with the names of each pair of candidates for President and Vice President of the 2019 Elections totaling 800 datasets. From the study with 800 datasets the best accuracy was obtained with a value of 92.5% with 80% training data composition and 20% testing data with a Precision value in each class between 85.7% - 97.2% and Recall value for each class among 78, 2% - 93.5%. With the Lexicon Based method as a labeling dataset, the process of labeling the Support Vector Machine dataset is no longer done manually but is processed by the Lexicon Based method and the dictionary on the lexicon can be added along with the development of data content on Twitter social media.

scholarly journals (The Impact of Traffic Accidents on Development: A Contemporary Accounting Perspective, Asir governorate as a model - Saudi Arabia (2006-2013: أثر حوادث المرور على التنمية نظرة محاسبية معاصرة محافظة (عسير) نموذجاً المملكة العربية السعودية (2006– 2013)

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Shahabuddin Mohammed Ahmed Abdullah
Keyword(s):  
Saudi Arabia ◽  
Community Development ◽  
Traffic Accidents ◽  
Kingdom Of Saudi Arabia ◽  
The Government ◽  
The Cost ◽  
The Impact

The traffic accidents in the high ways and towns are still increasing, their effect on the community development clearly seen. The control of this problem is highly significant. The analysis of the data and the information about the traffic accidents, their direct, indirect, a variables and continues cost represented in curing the injured, paying the Diya, the cost of the medical operations on behalf of the government and the relatives of the injured dealt with through the accounting view. This paper aimed at measuring the effect of traffic accidents in terms of money, to be use for the development of Accer province – South of the kingdom of Saudi Arabia. The overall cost of the traffic accidents in 2013 is 23 pillions Riyal. The percentage of the injured is 30% per family. The cost account of traffic accidents in Accer province is 1. 6 pillions Riyal. These sums of money could have been use for the development of the province. The paper recommends The direct, indirect, a variables and continues costs of the traffic accidents should give a due consideration The traffic administration should give a due consideration as well, to be minimizing the number of the traffic accidents. There should be decisive practical measures to stop these accidents.

scholarly journals Twitter sentiment analysis of the relocation of Indonesia's capital city

2020 ◽  
Vol 9 (4) ◽  
pp. 1620-1630
Author(s):  
Edi Sutoyo ◽  
Ahmad Almaarif
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Point Of View ◽  
Capital City ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Algorithm ◽  
The Government ◽  
The Many

Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media. 

scholarly journals An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan

2021 ◽  
Vol 11 (19) ◽  
pp. 9080
Author(s):  
Ruba Obiedat ◽  
Osama Harfoushi ◽  
Raneem Qaddoura ◽  
Laila Al-Qaisi ◽  
Ala’ M. Al-Zoubi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Sentiment Analysis ◽  
Support System ◽  
Support Vector ◽  
Whale Optimization ◽  
Vector Machines ◽  
Standard Classification ◽  
The Government ◽  
F Measure

The world has witnessed recently a global outbreak of coronavirus disease (COVID-19). This pandemic has affected many countries and has resulted in worldwide health concerns, thus governments are attempting to reduce its spread and impact on different aspects of life such as health, economics, education, and politics by making emergent decisions and policies (e.g., lockdown and social distancing). These new regulations influenced people’s daily life and cast significant burdens, concerns, and disparities on various population groups. Taking the wrong actions and enforcing bad decisions by some countries result in increasing the contagion rate and more catastrophic results. People start to post their opinions and feelings about their government’s decisions on different social media networks, and the data received through these platforms present a very useful source of information that affects how governments perceive and cope with the current the pandemic. Jordan was one of the top affected countries. In this paper, we proposed a decision support system based on the sentiment analysis mechanism by combining support vector machines with a whale optimization algorithm for automatically tuning the hyperparameters and performing feature weighting. The work is based on a hybrid evolutionary approach that aims to perform sentiment analysis combined with a decision support system to study people’s posts on Facebook to investigate their attitudes and feelings toward the government’s decisions during the pandemic. The government regulations were divided into two periods: the first and latter regulations. Studying public sentiments during these periods allows decision-makers in the government to sense people’s feelings, alert them in case of possible threats, and help in making proactive actions if needed to better handle the current pandemic situation. Five different versions were generated for each of the two collected datasets. The results demonstrate the superiority of the proposed Whale Optimization Algorithm & Support Vector Machines (WOA-SVM) against other metaheuristic algorithms and standard classification models as WOA-SVM has achieved 78.78% in terms of accuracy and 84.64% in term of f-measure, while other standard classification models such as NB, k-NN, J84, and SVM achieved an accuracy of 69.25%, 69.78%, 70.17%, and 69.29%, respectively, with 64.15%, 62.90%, 60.51%, and 59.09% F-measure. Moreover, when comparing our proposed WOA-SVM approach with other metaheuristic algorithms, which are GA-SVM, PSO-SVM, and MVO-SVM, WOA-SVM proved to outperform the other approaches with results of 78.78% in terms of accuracy and 84.64% in terms of F-measure. Further, we investigate and analyze the most relevant features and their effect to improve the decision support system of government decisions.

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