Sentiment analysis of multi-social media platform about medical services using support machine vector

Social media contains robust source information of people’s viewpoints because everyone can give their opinion independently. Due to various types of social media, there are different methods for scrapping data because different social media have different regulations. This study aims to build senti...

Full description

Saved in:
Bibliographic Details
Main Authors: Nainggolan, Cindy H., Amalia, Amalia, Sharif, Amer, Lydia, Maya Silvi
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Classification
Data mining
Digital media
Health services
Medical personnel
Sentiment analysis
Social networks
Support vector machines
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Social media contains robust source information of people’s viewpoints because everyone can give their opinion independently. Due to various types of social media, there are different methods for scrapping data because different social media have different regulations. This study aims to build sentiment analysis with data sources in Bahasa Indonesia (Indonesian Language) from various social media platforms such as Twitter, Facebook, and Tiktok. A case study from this research regarding health workers’ services for the community on social media with the Support Vector Machine (SVM) classification algorithm. This study used the hashtag #Tenagamedis (in English: health workers). The methodology of this study contains data collecting, filtering, pre-processing, data labeling, and classification. The crawling data get a total of 4223 comments with a composition of 617 from Twitter, 1650 from Facebook, and 2001 from Tiktok. The data from Tiktok get the best accuracy which is 91%. Based on the test results, the amount of data and the balance of the training data determine the classification accuracy.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0199896