What is it about?

Smartwatches have become increasingly popular due to their ability to track human activities. The tracked information can be shared with other devices, such as smartphones, and used for scheduling, time management, and health management. Although several studies have focused on developing techniques for natural language text, users intention-to-recommend smartwatches have never been investigated. Consequently, the manufacturers, as well as potential buyers cannot get a holistic view of users’ perception of the smart device of their interest. Also, the non-availability of publicly available benchmark corpus has thwarted the development of intention mining techniques. Retrospectively, this study has proposed an approach for mining users’ intention to recommend smartwatches. In particular, we have employed an innovative approach, involving a screening processing and annotation guidelines, to develop the first-ever manually annotated corpus for mining intention-to-recommend smartwatches. Furthermore, we have performed experiments using two deep-learning techniques and five types of word embeddings to evaluate their effectiveness for intention mining. Finally, the recommendation sentences are synthesized to develop a deeper understanding of the user feedback on the selected products.

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Why is it important?

1- We have developed the first ever benchmark corpus for investigating users’ intention-to-recommend smartwatches 2- We have used two state-of-the-art deep learning techniques(CNN,RNN) and five types of word embeddings. 3- The synthesis and the deeper understanding of the recommendation sentences revealed: (a) positive perception of the devices (b) users intention-to-recommend the devices to family and friends.

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This page is a summary of: Intention mining: A deep learning-based approach for smart devices, Journal of Ambient Intelligence and Smart Environments, January 2020, IOS Press, DOI: 10.3233/ais-200545.
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