Analisis Sentimen Terhadap Karir E-Sports Di Indonesia Pada Media Twitter Menggunakan Metode Naive Bayes

Abstract

The rapid growth of eSports has garnered significant attention worldwide, including in Indonesia. This study aims to perform sentiment analysis on the career of eSports in Indonesia using Twitter as the primary data source. The objective is to analyze the sentiment expressed by Twitter users towards eSports careers and identify the prevailing sentiment trends. The Naive Bayes method is employed as the classification algorithm for sentiment analysis. The study involves collecting a large dataset of tweets related to eSports careers in Indonesia and manually annotating them with sentiment labels. Preprocessing techniques are applied to clean the data and extract relevant features. The Naive Bayes classifier is trained on a labeled dataset to learn the relationships between features and sentiment labels. The trained classifier is then utilized to classify new tweets and determine their sentiment polarity. The results of this analysis provide insights into the sentiment towards eSports careers in Indonesia, including positive, negative, and neutral sentiments. The findings can be utilized by stakeholders in the eSports industry to gain a better understanding of public perception and make informed decisions regarding career development and marketing strategies. The evaluation of the sentiment analysis model is conducted using metrics such as accuracy, precision, recall, and F1-score. The achieved accuracy rate demonstrates the effectiveness of the Naive Bayes method in sentiment analysis of eSports careers on Twitter in the Indonesian context. This study contributes to the field of sentiment analysis in the eSports domain and provides valuable insights for industry professionals and researchers.