A Comprehensive Analysis of Indihome Customer Satisfaction Using Topic-Driven Sentiment Analysis

Yusza Reditya Murti, Aryo Nitiyoga, Herry Irawan, Aisyah Fadillah Al-Adawiyah, Edward Kandia Tenggehi

Abstract


The industrial revolution has advanced to version 4.0, with digital usage rising significantly. Social media, a key platform for quickly disseminating information, plays a major role in this shift. Information on social media often includes opinions, feedback, and suggestions for products, collectively known as sentiment. Sentiments can be positive, negative. This study aims to analyze consumer sentiment towards IndiHome's products and services through Twitter. Using IndoBERT, a deep learning model based on the BERT architecture, sentiment analysis is conducted. The research process involves data collection, preprocessing, and sentiment detection via IndoBERT, followed by training and testing. Results show that negative sentiment towards IndiHome is more prevalent than positive and neutral, with an accuracy above 80%. Furthermore, Topic-Driven Sentiment Analysis (TDSA) is employed to link sentiments to specific topics using Latent Dirichlet Allocation (LDA) for topic modeling. This approach helps to understand how different aspects of IndiHome’s products and services are discussed in relation to sentiments. The findings suggest that negative sentiment about IndiHome’s services and products is substantial, indicating customer dissatisfaction with the company’s offerings.

Keywords


Customer Satisfaction; Text Mining; Sentiment Analysis; Topic Modelling; IndoBERT

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DOI: https://doi.org/10.17509/seict.v6i2.93747

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