Abstract
Reviews are a person's way of expressing feedback on something in the form of criticism and ideas. Reviews of mobile apps are a type of user feedback that focuses on the performance and look of a mobile application and is typically featured on the download page of a mobile application, such as in the Apps Store. Because it comprises a person's feelings and emotions, whether they are joyful, sad, hostile, or indifferent toward a mobile application, the review data is textual and may be gathered and utilized as material for creating a textual dataset. This work creates a multi-label multi-class Indonesian-language dataset based on public reviews of mobile applications with sentiment and emotional values. Another factor supporting the creation of this dataset is the fact that there is still a limited number of textual datasets based on the Indonesian language that are multi-label multiclass for performing sentiment analysis tasks, particularly those linked to text classification tasks. The data generated by this research was cleaned and handled during the pre-processing step and was annotated with 3 sentiments, namely positive, negative, and neutral, as well as 6 emotions, namely anger, fear, sad, happy, love, and neutral.