Aspect-Based Sentiment Analysis and Business Intelligence Visualization of F&B Industry Beverage Product Customer Reviews from Instagram and Google Reviews
Keywords:
Sentiment Analysis, Aspect-Based Sentiment Analysis, Business Intelligence, Customer Reviews, Food and Beverage IndustryAbstract
The development of social media and online review platforms has made customer opinions an important source of data in understanding customer experiences, especially in the Food and Beverage (F&B) industry. However, most of this review data is unstructured and has not been optimally utilized in data-driven decision support systems. This study aims to analyze customer sentiment in greater depth through an Aspect-Based Sentiment Analysis (ABSA) approach and visualize the results using Business Intelligence (BI) to map customer experiences with F&B beverage products. This study uses a quantitative approach by analyzing 50 customer reviews sourced from Instagram and Google Reviews. The research stages include text preprocessing using Natural Language Processing (NLP), aspect and sentiment analysis using rule-based ABSA, sentiment classification using the Multinomial Naïve Bayes and Support Vector Machine (SVM) algorithms, and visualization of the results using the Business Intelligence platform. The results show that taste and price are the most dominant aspects discussed by customers. Taste and service are dominated by positive sentiment, while price, packaging, and portion size tend to receive negative sentiment.
The model evaluation shows that Multinomial Naïve Bayes produces the highest accuracy of 96.0%, while SVM produces an accuracy of 82.5%. BI visualization successfully displays sentiment distribution, aspect frequency, and aspect performance in an informative manner. This study proves that the integration of ABSA and BI is effective in converting unstructured review data into data-driven strategic insights. The results of the study successfully answered the research objectives and showed that this approach can be used as a basis for decision making to improve product and service quality. Further research is recommended to use a larger dataset and apply deep learning methods to improve the accuracy of the analysis.
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