Sentiment Analysis of the Use of Telecommunication Providers on Twitter Social Media using Convolutional Neural Network

Telecommunication technology continues to develop starting from 1G, 2G, 3G, 4G, and currently entering the 5G era. The Global System for Mobile Communications (GSM) based telecommunication industry in Indonesia consists of three big names: Telkomsel, XL, and Indosat. During the Covid-19 pandemic, activities carried out outside the home should be done online. People hope that the internet network can work properly. However, the reality is not as expected, because many networks are experiencing slow internet problems and many complaints are delivered through social media. Therefore, this research aims to find the insight opinions that have been conveyed to the telecommunications operator in social media. This research used the Convolutional Neural Network (CNN) algorithm to classify text sentiment (negative or positive) about telecommunication providers. The experiment with text data from Twitter is conducted after preprocessing and weighting of the Word2Vec process. The confusion matrix experiment shows that the CNN algorithm’s performance reaches an average accuracy value of around 86.22%. The experiment was carried out by dividing the training data and testing the data 5 times in 10 times. The study results indicated that disruption of cellular telecommunications operators provided many sentiments, especially negative sentiment at the beginning of the COVID-19 pandemic.