AWARE (AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation)

The peer-reviewed paper of AWARE dataset is published in ASEW 2021, and can be accessed through: http://doi.org/10.1109/ASEW52652.2021.00049. Kindly cite this paper when using AWARE dataset.

Aspect-Based Sentiment Analysis (ABSA) aims to identify the opinion (sentiment) with respect to a specific aspect. Since there is a lack of smartphone apps reviews dataset that is annotated to support the ABSA task, we present AWARE: ABSA Warehouse of Apps REviews.

AWARE contains apps reviews from three different domains (Productivity, Social Networking, and Games), as each domain has its distinct functionalities and audience. Each sentence is annotated with three labels, as follows:

Aspect Term: a term that exists in the sentence and describes an aspect of the app that is expressed by the sentiment. A term value of “N/A” means that the term is not explicitly mentioned in the sentence. Aspect Category: one of the pre-defined set of domain-specific categories that represent an aspect of the app (e.g., security, usability, etc.). Sentiment: positive or negative. Note: games domain does not contain aspect terms.

We provide a comprehensive dataset of 11323 sentences from the three domains, where each sentence is additionally annotated with a Boolean value indicating whether the sentence expresses a positive/negative opinion. In addition, we provide three separate datasets, one for each domain, containing only sentences that express opinions. The file named “AWARE_metadata.csv” contains a description of the dataset’s columns.

How AWARE can be used?

We designed AWARE such that it can be used to serve various tasks. The tasks can be, but are not limited to:

Sentiment Analysis. Aspect Term Extraction. Aspect Category Classification. Aspect Sentiment Analysis. Explicit/Implicit Aspect Term Classification. Opinion/Not-Opinion Classification. Furthermore, researchers can experiment with and investigate the effects of different domains on users' feedback.