Twitter Opinion Mining Analysis of Web-Based Handphone Brand Using Naïve Bayes Classification Method

Authors

    Suryadi Wijaya( 1 ) Yo Ceng Giap( 2 )

    (1) Universitas Buddhi Dharma
    (2) Universitas Buddhi Dharma

DOI:


https://doi.org/10.32877/bt.v4i2.287

Keywords:


Social Media, Twitter, Handphone, Sentiment Analysis, Naïve Bayes Classification Method

Abstract

Social Media is now very commonly used for the benefit of society. People mostly use social media to convey information, give opinions, even for media to express themselves. One of the social media that is widely used to convey this information is Twitter. From the use of Twitter, a public opinion tweet emerged about a mobile phone product. The more that is posted on Twitter about cellphones, the more public opinion will arise about cellphone brands. From these opinions, a classification is needed that can distinguish Neutral, Negative, or Positive Opinions. Sentiment analysis or opinion mining is one part of text mining that can help with these problems. In connection with the above, an application is designed that can analyze sentiment analysis from Twitter using the Naïve Bayes classification method. The results of the application of the Naïve Bayes classification method will result in a classification of sentiments into neutral, negative, or positive opinions

Downloads

Download data is not yet available.

Downloads

Published

2021-12-30

How to Cite

[1]
S. Wijaya and Y. Ceng Giap, “Twitter Opinion Mining Analysis of Web-Based Handphone Brand Using Naïve Bayes Classification Method”, bit-Tech, vol. 4, no. 2, pp. 56–60, Dec. 2021.

Issue

Section

Articles
DOI : https://doi.org/10.32877/bt.v4i2.287
Abstract views: 264 / PDF downloads: 153