A Simple Data Sentiment Analysis using Bjorka phenomenon on Twitter
DOI:
https://doi.org/10.11594/nstp.2023.3353Keywords:
Sentiment analysis, Bjorka, Twitter, modellingAbstract
Social media is one of the means used by netizens to access, share and discuss the latest and hottest news issues. Twitter as one of the social media is a platform that in real-time is often chosen to communicate that matter. Through sentiment analysis with the text method mining on Twitter, we can understand how people describe and express their perceptions of obesity both positively and negatively nor neutral. This analysis is important to see the extent to which social media such as Twitter is used today. Those are one of the instruments for disseminating information data security in Indonesia. Research objectives for identifying sentiment analysis on related Twitter the Bjorka phenomenon in Indonesia using the text mining method. The type of research is cross-sectional. This research plan was chosen because of the data taken from Twitter in the last four-month time series (June 2022 - October 2022). The result of web scraping on Twitter is 998 Indonesian tweets. Taking data using the Twitter Scraping extension pack and analyzing using Python 3.7.2. Based on the results of sentiment analysis tweets got a neutral sentiment of 744 (75%) tweets, followed by negative sentiment of as much as 175 (18%) tweets and positive sentiment by the number 75 (8%) of a total of 994 tweets. The conclusion was presented the modelling in based on the topic, and we got three topic most relevant terms for topic 0, 1, or 2 with 35,3%, 33%, 31,7% of tokens, respectively.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Prismahardi Aji Riyantoko, Amri Muhaimin
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this proceedings agree to the following terms:
Authors retain copyright and grant the Nusantara Science and Technology Proceedings right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this proceeding.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the proceedings published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this proceeding.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See the Effect of Open Access).
