Sentiment Analysis of Consumer Acceptance of Honda’s Digital Marketing Strategy Using Lexicon-Based Algorithm - Dias, Bartolomius; Khoirunnisa’, Asma’; Budiman, Yosef; Setiyawami, Setiyawami (Journal of Information System and Informatics, 2025)
This study analyzes customer sentiment toward Honda’s digital marketing strategy via the Wahana Honda application. A total of 2,000 customer reviews were collected from the Google Play Store using web-scraping techniques. Text data underwent preprocessing (e.g. cleansing, tokenization, stop-word removal, stemming, and translation into English). Sentiment classification using a lexicon-based approach revealed that 56.7% of reviews were positive, 20.8% neutral, and 22.5% negative. The model demonstrated high precision in identifying negative sentiment, though it showed limitations in classifying neutral opinions due to linguistic ambiguity. These findings highlight the need for more adaptive sentiment models and offer strategic insights for Honda’s digital marketing. Specifically, the analysis can help prioritize improvements in app functionality, excellence service priority, enhance personalized customer engagement, and shape targeted digital marketing strategies based on real user feedback. Leveraging these insights enables Honda to optimize user experience, increase retention, and align digital campaigns with customer expectations.