Aspect-based sentiment analysis in smart devices: A comprehensive and specialized dataset.

In the ever-evolving landscape of smart devices, understanding user sentiments is crucial for refining technology and enhancing user experiences. This research presents a novel aspect-based sentiment analysis dataset in the domain of smart devices. The dataset compiles user reviews from diverse USA-based benchmark websites like Amazon, Target, and Walmart. The dataset contains 2370 reviews and offers a well-balanced sentiment distribution with 842 positive, 800 negative, and 728 neutral reviews. To identify key aspects for sentiment analysis, a consultative approach was employed, engaging both industry professionals and end-users. By employing a consultative approach, key aspects such as 'Clock,' 'Connectivity,' and 'Sound' along with other distinctive aspects are identified in user reviews. Covering 10 different smart devices and encompassing approximately 30 distinct aspects, this dataset not only provides comprehensive insights into user sentiments but also serves as valuable training data for machine learning and deep learning models in the realm of sentiment analysis. This research contributes a foundational resource for future research into the landscape of user experiences with smart devices.