When Homecoming is not Coming: 2021 Homecoming Ban Sentiment Analysis on Twitter Data Using Support Vector Machine Algorithm

2021 ◽  
Author(s):  
Lidia Sandra ◽  
Ford Lumbangaol
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Twitter Data

A New SVM Method for Recognizing Polarity of Sentiments in Twitter

2017 ◽  
pp. 281-291 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Marenglen Biba ◽  
Rohan Kumar ◽  
Rahul Kumar ◽  
Pijush Samui
Keyword(s):  
Social Networks ◽  
Support Vector Machine ◽  
Public Relations ◽  
Social Networking ◽  
Sentiment Analysis ◽  
High Accuracy ◽  
Support Vector ◽  
Online Social Networking ◽  
Web Page ◽  
Twitter Data

Online social networking platforms, such as Weblogs, micro blogs, and social networks are intensively being utilized daily to express individual's thinking. This permits scientists to collect huge amounts of data and extract significant knowledge regarding the sentiments of a large number of people at a scale that was essentially impractical a couple of years back. Therefore, these days, sentiment analysis has the potential to learn sentiments towards persons, object and occasions. Twitter has increasingly become a significant social networking platform where people post messages of up to 140 characters known as ‘Tweets'. Tweets have become the preferred medium for the marketing sector as users can instantly indicate customer success or indicate public relations disaster far more quickly than a web page or traditional media does. In this paper, we have analyzed twitter data and have predicted positive and negative tweets with high accuracy rate using support vector machine (SVM).

scholarly journals PERBANDINGAN KERNEL SUPPORT VECTOR MACHINE (SVM) DALAM PENERAPAN ANALISIS SENTIMEN VAKSINISASI COVID-19

2021 ◽  
Vol 4 (2) ◽  
pp. 139-145
Author(s):  
Thalita Meisya Permata Aulia ◽  
Nur Arifin ◽  
Rini Mayasari
Keyword(s):  
Clinical Trial ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Research Data ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Kernel Space ◽  
Kernel Support Vector Machine ◽  
Pros And Cons

In early 2020, the first recorded death from the COVID-19 virus in China [3]. Followed by WHO which later stated that the COVID-19 virus caused a pandemic. Various efforts were made to minimize the transmission of COVID-19, such as physical distancing and large-scale social circulation. However, this resulted in a paralyzed economy, many factories or business shops closed, eliminating the livelihoods of many people. Vaccines may be a solution, various International Research Communities have conducted research on the COVID-19 vaccine. In early 2021 the Sinovac vaccine from China arrived in Indonesia and was declared a BPOM clinical trial, but the existence of the vaccine still raises pros and cons, some have responded well and others have not. For this reason, a sentiment analysis of the COVID-19 vaccine will be carried out by taking data from Twitter, then classified using the Support Vector Machine algorithm. The research data is nonlinear data so it requires a kernel space for the text mining process, while there has been no specific research regarding which kernel is good for sentiment analysis, so a test will be carried out to find the best kernel among linear, sigmoid, polynomial, and RBF kernels. The result is that sigmoid and linear kernels have a better value, namely 0.87 compared to RBF and polynomial, namely 0.86

scholarly journals Twitter Sentiment Analysis of Bangkok Tourism During COVID-19 Pandemic Using Support Vector Machine Algorithm

2021 ◽  
Vol 16 (1) ◽  
pp. 24-30
Author(s):  
Thanapat Sontayasara ◽  
Sirawit Jariyapongpaiboon ◽  
Arnon Promjun ◽  
Napat Seelpipat ◽  
Kumpol Saengtabtim ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Tourism Industry ◽  
Capital City ◽  
World Health ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Control And Prevention ◽  
Primary Focus ◽  
Health Organization

In the year 2020, SARS-CoV-2, the virus behind the coronavirus disease (COVID-19) pandemic, affected many lives and businesses worldwide. COVID-19, which originated in Wuhan City, China, at the end of December 2019, spread over the entire world in approximately four months. By October 2020, approximately 20 million people were infected and millions had died from this disease. Many health organizations such as the World Health Organization and Centers for Disease Control and Prevention made COVID-19 their primary focus. Many industries, especially, the tourism industry, were affected by the pandemic as many flight and hotel reservations were canceled. Thailand, a country considered one of the world’s most popular tourist destinations, suffered much losses because of this pandemic. Many events and travel bookings were canceled and/or postponed. Many people expressed their views and emotions related to this situation over social media, which is considered a powerful media for spreading news and information. In this research, the views of people who were planning to travel to Bangkok, the capital city and most popular destination in Thailand, were retrieved from Twitter for the dates between April 3 and 30, 2020, the period during which the country underwent nationwide lockdown. Sentiment analysis was performed using the support vector machine algorithm. The results showed 71.03% classification accuracy based on three sentiment classifications: positive, negative, and neutral. This study could thus provide an insight into travelers’ opinions and sentiments related to the tourism business. Based on the significant terms in each sentiment extracted, strengths and weaknesses of each tourism issue could be obtained, which could be used for making recommendations to the related tourism organizations.

scholarly journals USTADZ ABDUL SOMAD LECTURE SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE ALGORITHM COMPARISON OF COMPARATIVE FEATURES SELECTION

2020 ◽  
Vol 16 (1) ◽  
pp. 111-116
Author(s):  
Dedi Aridarma ◽  
Rifki Sadikin ◽  
Bobby Suryo Prakoso ◽  
Heru Sukma Utama
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Information Gain ◽  
Particle Swarm ◽  
Support Vector ◽  
Features Selection ◽  
Support Vector Machine Algorithm ◽  
Swarm Optimization ◽  
Algorithm Comparison

Religious lectures are activities that are identical to the religious presentation, delivered verbally by a person who has religious knowledge and then delivered to the community with the aim of the knowledge delivered can be understood. Ustadz Abdul Somad was one of the preachers who had been known to various levels of society, but his lectures were not all acceptable to the people who liked or disliked those who came from various positive and negative comments on social media. To solve these problems, Sentiment Analysis was used by applying the Support Vector Machine Algorithm method. The purpose of this study is to compile using the selection of feature Particle Swarm Optimization and Information Gain. The results for Particle Swarm Optimization Selection Feature resulted in Accuracy of 80.57%, Precision of 85.45%, and Recall of 79.52%, Selection Feature Information Gain resulted in Accuracy of 79.78%, Precision of 78.47%, and Recall of 78, 43%, Based on the results of this study, it can be concluded that using the Particle Swarm Optimization selection feature is better at the level of accuracy when compared to using the Information Gain selection feature.

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