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.

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 Naïve Bayes Classifiers For Tweet Sentiment Analysis Using GPU

2019 ◽  
Vol 8 (5C) ◽  
pp. 1470-1472
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Classification Model ◽  
Communication Tool ◽  
Bayes Method ◽  
Internet Users ◽  
National Media ◽  
Sequential Classification

The use of computers to solve problems has been done for all areas of work. Along with this, demanded faster computing process. To perform sentiment analysis of data obtained from the internet. Data taken from micro-blogging which at this time became the most popular communication tool and favored by internet users. The method used to construct the classification model of training data in this research is Naive Bayes Method. Training data is collected by utilizing the crontab facility with query emoticons and national media accounts linked to the Twitter API. The collected data will pass certain preprocessing before the training. The weighting feature used is the term frequency with TF-IDF. All data used in this research is a tweet that is delivered in Bahasa Indonesia. From the implementation results obtained 96.61% accuracy for sequential classification conducted using GPU GeForce 930M.

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.

The Use of Social Media by the Botswana Government and Governance of Liquid Communication

Mousaion ◽  
2019 ◽  
Vol 37 (1) ◽  
Author(s):  
Tshepho Lydia Mosweu
Keyword(s):  
Social Media ◽  
Customer Service ◽  
Community Involvement ◽  
Media Content ◽  
Service Access ◽  
Communication Tool ◽  
African Countries ◽  
The World ◽  
The Government ◽  
Use Of Social Media

Social media as a communication tool has enabled governments around the world to interact with citizens for customer service, access to information and to direct community involvement needs. The trends around the world show recognition by governments that social media content may constitute records and should be managed accordingly. The literature shows that governments and organisations in other countries, particularly in Europe, have social media policies and strategies to guide the management of social media content, but there is less evidence among African countries. Thus the purpose of this paper is to examine the extent of usage of social media by the Botswana government in order to determine the necessity for the governance of liquid communication. Liquid communication here refers to the type of communication that goes easily back and forth between participants involved through social media. The ARMA principle of availability requires that where there is information governance, an organisation shall maintain its information assets in a manner that ensures their timely, efficient and accurate retrieval. The study adopted a qualitative case study approach where data were collected through documentary reviews and interviews among purposively selected employees of the Botswana government. This study revealed that the Botswana government has been actively using social media platforms to interact with its citizens since 2011 for increased access, usage and awareness of services offered by the government. Nonetheless, the study revealed that the government had no official documentation on the use of social media, and policies and strategies that dealt with the governance of liquid communication. This study recommends the governance of liquid communication to ensure timely, efficient and accurate retrieval when needed for business purposes.

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.

Web Text Mining

2016 ◽  
Author(s):  
Ricardo Baeza-Yates ◽  
Roi Blanco ◽  
Malú Castellanos
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Web Search ◽  
Internet Users ◽  
Entity Retrieval ◽  
Web Text Mining ◽  
Text Content ◽  
The Web

Web search has become a ubiquitous commodity for Internet users. This fact puts a large number of documents with plenty of text content at our fingertips. To make good use of this data, we need to mine web text. This triggers the two problems covered here: sentiment analysis and entity retrieval in the context of the Web. The first problem answers the question of what people think about a given product or a topic, in particular sentiment analysis in social media. The second problem addresses the issue of solving certain enquiries precisely by returning a particular object: for instance, where the next concert of my favourite band will be or who the best cooks are in a particular region. Where to find these objects and how to retrieve, rank, and display them are tasks related to the entity retrieval problem.

Sentiment Analysis with Social Media Analytics, Methods, Process, and Applications

2017 ◽  
pp. 192-208
Author(s):  
Karteek Ramalinga Ponnuru ◽  
Rashik Gupta ◽  
Shrawan Kumar Trivedi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Social Media Analytics ◽  
Nearest Neighbour ◽  
Huge Amount ◽  
Word Clouds

Firms are turning their eye towards social media analytics to get to know what people are really talking about their firm or their product. With the huge amount of buzz being created online about anything and everything social media has become ‘the' platform of the day to understand what public on a whole are talking about a particular product and the process of converting all the talking into valuable information is called Sentiment Analysis. Sentiment Analysis is a process of identifying and categorizing a piece of text into positive or negative so as to understand the sentiment of the users. This chapter would take the reader through basic sentiment classifiers like building word clouds, commonality clouds, dendrograms and comparison clouds to advanced algorithms like K Nearest Neighbour, Naïve Biased Algorithm and Support Vector Machine.

Sentiment Analysis in Crisis Situations for Better Connected Government

2021 ◽  
pp. 162-181
Author(s):  
Asdrúbal López Chau ◽  
David Valle-Cruz ◽  
Rodrigo Sandoval-Almazán
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Citizen Participation ◽  
Important Means ◽  
The Government ◽  
Made In ◽  
Crisis Situations

One of the pillars of connected government is citizen centricity: an approach in which citizen participation is essential. In Mexico, social networks are currently one of the most important means by which citizens express their needs and provide opinions to the government. The goal of this chapter is to contribute to citizen centricity by adapting the methodology of sentiment analysis of social media posts to an expanded version for crisis situations. The main difference in this approach from the normally accepted one is that instead of using pre-defined classes (positive and negative) for sentiments, the authors first determined the different data categories and then applied them to the classic process of sentiment analysis. This approach was tested using posts on Mexico's earthquake in 2017. They found that needs, demands, and claims made in the posts reflect sentiments in a better way, and this can help to improve the government-citizen connection.

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