Analysis of Twitter Sentiment in Cases Of Domestic Violence Comparison of Lexion-Based and Niave-Bayes

Ardi Wijaya ; Rozali Toyib ; Jestika Safitri ; Anisya Sonita ; Yulia Darnita
International Journal of Information Technology an... 📅 2024 Vol 7, No 1 🔗 DOI: 10.24246/ijiteb.712024.01-08
Abstract
Twitter, a social media platform with millions of users, serves as a valuable source for unique insights. The case of Lestibillar domestic violence has garnered attention, fueling various circulating rumors that encompass positive, negative, and neutral opinions. This, in turn, gives rise to the potential spread of fake news. To counter this, sentiment analysis is employed using machine learning techniques. In this research, two machine learning algorithms within the realm of supervised learning are compared: lexicon-based and Naive Bayes. Sentiment objects are created for each algorithm to facilitate the comparison, aiming to determine which algorithm performs better in terms of accuracy. The results of the calculations indicate that Naive Bayes outperforms, achieving a superior accuracy of 99.96%, while the lexicon-based method lags significantly behind at 10.29%. The dominance of positive tweets is evident, comprising 2709 out of the total tweets on Twitter.
Keywords
Machine learning; Twitter; Sentiment; Lexicon based naive bayes
How to Cite

Ardi Wijaya, et al. (2024). Analysis of Twitter Sentiment in Cases Of Domestic Violence Comparison of Lexion-Based and Niave-Bayes. International Journal of Information Technology and Business (IJITEB), 7(1). https://doi.org/10.24246/ijiteb.712024.01-08

Ardi Wijaya; Rozali Toyib; Jestika Safitri; Anisya Sonita; Yulia Darnita, "Analysis of Twitter Sentiment in Cases Of Domestic Violence Comparison of Lexion-Based and Niave-Bayes," International Journal of Information Technology and Business (IJITEB), vol. 7, no. 1, 2024.

Ardi Wijaya; Rozali Toyib; Jestika Safitri; Anisya Sonita; Yulia Darnita. "Analysis of Twitter Sentiment in Cases Of Domestic Violence Comparison of Lexion-Based and Niave-Bayes." International Journal of Information Technology and Business (IJITEB), vol. 7, no. 1, 2024.

Ardi Wijaya; Rozali Toyib; Jestika Safitri; Anisya Sonita; Yulia Darnita. "Analysis of Twitter Sentiment in Cases Of Domestic Violence Comparison of Lexion-Based and Niave-Bayes." International Journal of Information Technology and Business (IJITEB) 7, no. 1 (2024).

Ardi Wijaya, et al. (2024) 'Analysis of Twitter Sentiment in Cases Of Domestic Violence Comparison of Lexion-Based and Niave-Bayes', International Journal of Information Technology and Business (IJITEB), 7(1). doi: 10.24246/ijiteb.712024.01-08.

Ardi Wijaya; Rozali Toyib; Jestika Safitri; Anisya Sonita; Yulia Darnita. Analysis of Twitter Sentiment in Cases Of Domestic Violence Comparison of Lexion-Based and Niave-Bayes. International Journal of Information Technology and Business (IJITEB). 2024;7(1).

Artikel Terkait

Resilience of Deep Q-Network (DQN) Agent in Mitigating Ethereum Trading Risks Under Bearish Market Conditions

Adha, Fajri; Widiastuti, Tiwuk; Selvian Djahi, Bertha

An Adaptive AI-Driven Copywriting Framework: Design, Implementation, and Evaluation of a Web-Based GPT-Integrated Content Generation System

Daru, April Firman; Christanto, Febrian Wahyu; Prathivi, Rastri; Prasetyo, Dimas; Firdaus, Eryan Ahmad

Multi-Objective Reinforcement Learning for Supply Chain Optimization in Indonesia’s Free Nutritious Meal Program

Yanto, Budi; Saragih, Rusmin; Lubis, Adyanata; Elyandri Prasiwiningrum; Wahyuny, Romy

Modeling Factors Affecting Vocational School Students’ Perceived Ease of Use of Educational Technology

Hafid, Masruri; Krismiyati, Krismiyati; Diana, Andrushia

Determination of Maintenance Priority Based on Analytical Hierarchy Process for Magnetic Resonance Imaging

Nurdono Nurdono; Muhamad Haddin

Customer Loyalty Analysis Using RFM Model and K-Means Clustering for Marketing Strategy Optimization

Sahertian, Vigo Yano; Yessica Nataliani
Tren Sitasi Jurnal