A Comparison of Indonesia E-Commerce Sentiment Analysis for Marketing Intelligence Effort

TL;DR

This paper explores sentiment analysis of Indonesian e-commerce customer feedback on Twitter using Naive Bayes and TF-IDF to assess customer satisfaction levels across top platforms.

Contribution

It demonstrates the effectiveness of Naive Bayes with TF-IDF in classifying social media sentiment for marketing intelligence in Indonesian e-commerce.

Findings

Elevenia has the highest customer satisfaction among the three platforms.

The method accurately classifies Twitter sentiment data.

Social media data is useful for real-time marketing insights.

Abstract

The rapid growth of the e-commerce market in Indonesia, making various e-commerce companies appear and there has been high competition among them. Marketing intelligence is an important activity to measure competitive position. One element of marketing intelligence is to assess customer satisfaction. Many Indonesian customers express their sense of satisfaction or dissatisfaction towards the company through social media. Hence, using social media data provides a new practical way to measure marketing intelligence effort. This research performs sentiment analysis using the naive bayes classifier classification method with TF-IDF weighting. We compare the sentiments towards of top-3 e-commerce sites visited companies, are Bukalapak, Tokopedia, and Elevenia. We use Twitter data for sentiment analysis because it's faster, cheaper, and easier from both the customer and the researcher side. The purpose of this research is to find out how to process the huge customer sentiment Twitter to become useful information for the e-commerce company, and which of those top-3 e-commerce companies has the highest level of customer satisfaction. The experiment results show the method can be used to classify customer sentiments in social media Twitter automatically and Elevenia is the highest e-commerce with customer satisfaction.