Sentimen Analisis Masyarakat Indonesia di Twitter Terkait Metaverse dengan Algoritma Support Vector Machine

Ahmad, Ali Hasan; Gata, Windu

doi:10.35870/jtik.v6i4.569

Cited by 9 publications

(9 citation statements)

References 2 publications

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“…Instead of a survey, the focus of our work is to use data visualization and machine learning to analyze and predict metaverse sentiment. Ahmad and Gata [11] analyzed the sentiment of Indonesian people towards the metaverse. They found that majority (66%) of the people showed neutral sentiment towards the technology and the remaining population were split between positive (16%) and negative (17%).…”

Section: Related Workmentioning

confidence: 99%

Inevitable-Metaverse: A Novel Twitter Dataset for Public Sentiments on Metaverse

Hayawi

Shahriar

Serhani³

et al. 2022

Preprint

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Metaverse has emerged as a novel technology with the objective to merge the physical world into the virtual world. This technology has seen a lot of interest and investment in recent times from prominent organizations including Facebook which has changed its company name to Meta with the goal of being the leader in developing this technology. Although people in general are excited about the prospects of metaverse due to potential use cases such as virtual meetings and virtual learning environments, there are also concerns due to potential negative consequences. For instance, people are concerned about their data privacy as well as spending a lot of their time on the metaverse leading to negative impacts in real life. Therefore, this research aims to further investigate the public sentiments regarding metaverse on social media. A total of 86565 metaverse-related tweets were used to perform lexicon-based sentiment analysis. Furthermore, various machine and deep learning models with various text features were utilized to predict the sentiment class. The BERT transformer model was demonstrated to be the best at predicting the sentiment categories with 92.6% accuracy and 0.91 F-measure on the test dataset. Finally, the implications and future research directions were also discussed.

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Section: Related Workmentioning

confidence: 99%

Inevitable-Metaverse: A Novel Twitter Dataset for Public Sentiments on Metaverse

Hayawi

Shahriar

Serhani³

et al. 2022

Preprint

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“…Sentiment analysis is an analysis technique that can be used to determine and measure the attitudes or emotions of each user toward a topic. In this context, sentiment analysis of Twitter users regarding metaverse technology may involve collecting and analyzing tweets about the metaverse to determine whether Twitter users generally show a positive, negative, or neutral attitude toward the topic (Ahmad & Gata, 2022). Previous research on Lexicon-based sentiment analysis usually focuses on how this method can be used for sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Twitter User Opinions Related to Metaverse Technology Using Lexicon Based Method

Akbari

Tukino

Huda

et al. 2023

SinkrOn

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The technology of the metaverse has been a topic of discussion in the tech sector recently, and the implementation of the metaverse in the future is one of the most anticipated things for internet users around the world, allowing users to become the avatar they want in the virtual universe. To see how someone approaches metaverse technology, we can analyze it by looking at various opinions that emerge from the sentiment of internet users, one of which is from the social media Twitter. This study aims to analyze the sentiment of Twitter users' opinions, which is one of the most popular internet social media platforms. The researcher collects tweet data using the snscrape library in the Python programming language, a tool for mass data collection from Twitter. Then, the analysis of the sentiment of Twitter user opinions is carried out using the Lexicon Based method, which is a method that uses a dictionary of words to categorize the sentiment of a text. The results of this study show that the Lexicon Based method can classify the sentiment of Twitter user opinions related to metaverse technology with an accuracy value of 84%. This shows that the results of sentiment analysis using the Lexicon Based method have a good level of accuracy in performance in analyzing the sentiment of Twitter user opinions related to metaverse technology.

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“…The Metaverse technology began to be widely discussed when the founder of Facebook, Mark Zuckerberg, officially transformed his product with the name Meta. His ideas regarding this technology were submitted at the end of June 2021 [1]. Metaverse is a threedimensional virtual world technology where Avatars or our profile representations in that world can carry out social, political, economic and cultural activities [2].…”

Section: Introductionmentioning

confidence: 99%

Data Mining Algorithm Testing For SAND Metaverse Forecasting

Julianto

Kurniadi

Nashrulloh

et al. 2022

JAIS

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Metaverse is a technology that allows us to buy virtual land. In the future life in the real world can be duplicated into the Metaverse to increase efficiency, effectiveness, and a world without being limited by space and time. To buy land in the Metaverse, one can be done by using SAND. SAND is a crypto asset from a game called The Sandbox which functions as a transaction tool where in that game we can buy land and build it for various purposes just like we can store our Non-Fungible Tokens there. Metaverse is a digital business that will promise in the future because it offers easy and fast transactions. This study aims to compare the exact algorithm for making predictions about the SAND cryptocurrency used to buy Metaverse land. 7 algorithms are being compared, namely Deep Learning, Linear Regression, Neural Networks, Support Vector Machines, Generalized Linear Models, Gaussian Process, and K-Nearest Neighbors. The research method used is Knowledge Discovery in Databases. The research results show that the Support Vector Machines Algorithm has the most optimal Root Means Square Error value, root_mean_squared_error: 0.022 +/- 0.062 (micro average: 0.062 +/- 0.000). Based on this comparison, the Support Vector Machines Algorithm is suitable for predicting SAND Metaverse prices.

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Sentimen Analisis Masyarakat Indonesia di Twitter Terkait Metaverse dengan Algoritma Support Vector Machine

Cited by 9 publications

References 2 publications

Inevitable-Metaverse: A Novel Twitter Dataset for Public Sentiments on Metaverse

Inevitable-Metaverse: A Novel Twitter Dataset for Public Sentiments on Metaverse

Sentiment Analysis of Twitter User Opinions Related to Metaverse Technology Using Lexicon Based Method

Data Mining Algorithm Testing For SAND Metaverse Forecasting

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