Study of Sentiment of Governor's Election Opinion in 2018-Reference-Cited by-同舟云学术

Study of Sentiment of Governor's Election Opinion in 2018

Published:2018-12-12 Issue: Volume: Page:231-238
ISSN:2394-4099
Container-title:International Journal of Scientific Research in Science, Engineering and Technology
language:en
Short-container-title:IJSRSET

Author:

Saputro Agung Eddy Suryo¹,Notodiputro Khairil Anwar¹,A Indahwati¹

Affiliation:

1. Department of Statistic, Bogor Agricultural University, Bogor, Indonesia

Abstract

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

Publisher

Technoscience Academy

Subject

General Medicine

Reference19 articles.

1. Kwak, H., Lee, C., Park, H., and Moon, S.. 2010. "What is Twitter , a Social Network or a News Media??”. International World Wide Web Conference Com-mittee (IW3C2). pp. 591–600.

2. Tumasjan, A., Sprenger, T.O., Sandner, P.G., and Welpe, I.M. 2010. "Predicting Elections with Twitter?: What 140 Characters Reveal about Political Sentiment”. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media Predicting. pp. 178–185.

3. Hanna, A., Wells, C., Maurer, P., Friedland, L., Shah, D., and Matthes, J. 2013. "Partisan Alignments and Political Polarization Online?: A Computational Approach to Understanding the French and US Presidential Elections”. PLEAD’13.

4. Indonesian Internet Service Providers Association. 2017. "Penetration & Behavior of Indonesian Internet Users".

5. Ding, X., Liu, B., and Yu, P.S. 2008. "A Holistic Lexicon-Based Approach to Opinion Mining,” Proceeding of the 2008 International Conference on Web Search and Data Mining. pp. 231–240.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The Emotion Analysis of Indian Political Tweets using Machine Learning;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2024-04-12

2. Aspect-Based Sentiment Analysis Applied in the News Domain Using Rule-Based Aspect Extraction and BiLSTM;2023 IEEE 6th International Conference on Computer and Communication Engineering Technology (CCET);2023-08-04

3. Sentiments analysis of fMRI using automatically generated stimuli labels under naturalistic paradigm;Scientific Reports;2023-05-04

4. Influence of Sentiment on Mandiri Bank Stocks (BMRI) Using Feature Expansion with FastText and Logistic Regression Classification;2022 International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS);2022-11-23