DEVELOPMENT OF A SENTIMENT ANALYSIS DASHBOARD FOR TOKOPEDIA USING MACHINE LEARNING AND COMPETITIVE INTELLIGENCE APPROACH

• Main Author: Habiburrahman, Farhan
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/86230
• Institution: Institut Teknologi Bandung

As e-commerce competition increases, e-commerce needs a system to constantly monitor and gain feedback from the market to gain and maintain competitive advantage. This research proposes a competitive intelligence (CI) system to facilitate e-commerce with tools to monitor their competitor and to help them in identifying problems. The CI system consists of a sentiment analysis model to process the user reviews and a dashboard to display insights of the processed data. The proposed competitive intelligence system utilizes Google Play Store user reviews from mobile applications of major e-commerce platforms in Indonesia as its primary data. Using Python library scraper, data such as user review, timestamps, and star rating are collected from Tokopedia, Shopee, Bukalapak, Lazada, and Blibli. After 50,000 user reviews are collected from each e-commerce, it is sampled and processed to be used for training the sentiment analysis model. The competitive intelligence system incorporates a machine learning-based sentiment analysis framework, utilizing advanced natural language processing models such as Bidirectional Encoder Representations from Transformers (BERT) to identify aspects (subject or features being discussed) within reviews along with identifying its sentiment. The training of the BERT model was optimized with Optuna, a software framework that automates the process of searching the best training hyperparameters to ensure the best model is generated. The sentiment analysis model performed well in aspect term extraction (ATE) and aspect polarity classification (APC) tasks. The final generated model achieved an F1 score of 85.04% for APC and 69.55% for ATE. The sentiment analysis result is combined with the original user reviews before passed into the dashboard. The dashboard is built using Power BI due to its ease of use and modular nature. The insights found from processed reviews such as sentiment trends, average monthly star ratings, and most mentioned aspects was presented through a dashboard. This system can automate user review processing and provide actionable insights for e-commerce. The created dashboard can offer insights from collected user reviews while also acting as a tool to monitor other competitors.