SENTIMENT ANALYSIS OF SERVICE PROVIDER ON TWITTER TWEET USING NAIVE BAYES CLASSIFIER WITH PHP

Juri Pebrianto

doi:10.47080/iftech.v5i2.2752

Juri Pebrianto Universitas Pamulang http://orcid.org/0000-0002-9345-4567

DOI: https://doi.org/10.47080/iftech.v5i2.2752

Keywords: Twitter, Sentiment Analysis, Naive Bayes, PHP

Abstract

This study aimed to analyze the sentiments of Twitter users towards internet services provided by Indosat, Ooredoo and XL Axiata in Indonesia. Twitter Tweet data is taken using the Twitter API and processed using the PHP programming language. The classification process uses the Naïve Bayes Classifier algorithm to group positive, negative, and neutral categories. The calculation results show that negative sentiment towards Indosat Ooredoo services scores 56%, while sentiment towards XL Axiata services negatively scores 50%. The accuracy of the sentiment analysis system using PHP reaches 78% based on comparison with manual classification results. This research provides benefits in the form of information about user sentiment towards cellular internet service providers, so that each provider can improve and optimize internet services based on this data.

Keywords: twitter, sentiment analysis, naïve bayes, php

Author Biography

Juri Pebrianto, Universitas Pamulang

Universitas Pamulang
Fakultas Ilmu Komputer
Program Studi Teknik Informatika

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