@phdthesis{eprintsunpak7677,
            year = {2023},
           title = {Sentiment Analysis Genshin Impact Game to Know Players 
Reaction and Expectation Using Support Vector Machine},
          school = {Universitas Pakuan},
           month = {February},
          author = {Muhammad Refansyach Sugianto and Arie Qur?ania and Fajar Delli W},
        abstract = {Sentiment Analysis Genshin Impact Game to Know Players 
Reaction and Expectation Using Support Vector Machine
Arie Qur?ania1
, Fajar Delli Wihartiko2
, Muhammad Refansyach Sugianto3
1Program Studi Ilmu Komputer, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas 
Pakuan, Bogor, Jawa Barat, 16143, Indonesia
Abstract
As the popularity of online games increases, it becomes important to know the sentiment of players 
towards a particular game title. This study aims to determine the reactions and expectations of players in 
community of game titled Genshin Impact, based on aspect of character, event and game as a whole in 
game version 3.6. In this case, sentiment analysis is used to classify players opinion on YouTube comment 
and determine whether the opinion is positive, neutral or negative. The data used are player comments on 
6 YouTube videos uploaded by the official Genshin Impact Channel that promoting the new version of 
the game, version 3.6. The process involved several steps like Scraping data comment, Cleaning, Case 
Folding, Tokenization, Filtering, Lemmatization, Feature Extraction using TF-IDF and Classification 
using Support Vector Machine (SVM). The results showed that the overall sentiment of players towards 
version 3.6 was positive. However, there are some player expectations and complaints that can be taken 
into consideration by developers. In the character aspect, players expect the new characters, Kaveh and 
Baizhu, to have a stronger appeal. On the game aspect, players want long-term content to keep them 
motivated to keep playing. On the event aspect, players complain that the events in this version seem very 
long which makes players quickly get bored. The classification model with the SVM Algorithm 
succeeded in providing a fairly high accuracy of 83\% in the game and character aspects, while in the 
event aspect the model provided a fairly good accuracy of 69\%.
Keywords: Sentiment Analysis; Genshin Impact; Version 3.6; expectations; SVM},
             url = {http://eprints.unpak.ac.id/7677/}
}