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

Authors

  • Wahyu Azriel Akbari Universitas Buana Perjuangan Karawang
  • Tukino Universitas Buana Perjuangan Karawang
  • Baenil Huda Universitas Buana Perjuangan Karawang
  • Muhamad Muslih Universitas Nusa Putra, Indonesia

DOI:

10.33395/sinkron.v8i1.11992

Keywords:

Lexicon Based; Metaverse Technology; Sentiment Analysis; Twitter; Python.

Abstract

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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How to Cite

Akbari, W. A. ., Tukino, T., Huda, B. ., & Muslih, M. . (2023). Sentiment Analysis of Twitter User Opinions Related to Metaverse Technology Using Lexicon Based Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(1), 195-201. https://doi.org/10.33395/sinkron.v8i1.11992

Issue

Vol. 8 No. 1 (2023): Articles Research Volume 8 Issue 1, 2023

Section

Articles

License

Copyright (c) 2023 Wahyu Azriel Akbari, Tukino, Baenil Huda, Muhamad Muslih

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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