This study examines the application of sentiment analysis as an approach in the Multi-Criteria Decision Analysis (MCDA) method to create mapping criteria and sub-criteria related to waste management in Indonesia. Sentiment analysis techniques are used to identify relevant features from social media reviews and recognize sentiment polarity, which are then utilized as criteria and weights in hierarchical analysis using the AHP method. This study uses Aspect-Based Sentiment Analysis (ABSA) based on Support Vector Machine (SVM) to create model on waste management sentiment using data in the Indonesian language. The result shows that the best accuracy 0.97 is for the Economy criteria, followed by Waste type 0.88, Activities 0.86, Facilities 0.76, and Social 0.65. The MCDA-AHP results show that at the criteria level, activities are first in priority, followed by social, waste types, facilities, and economy. At the sub-criteria level, profit, inorganic, and initiatives have the highest priority weights. In conclusion, this study yields two primary results: a categorization model for the waste management topic and priority weights of criteria and sub-criteria based on public opinion. These results show that dominating positive sentiments generate bigger weight values, which can be used to view the importance of criteria and sub-criteria and gauge public preferences.