no code implementations • 24 Feb 2021 • Amir Mohammad Naderi, Haisong Bu, Jingcheng Su, Mao-Hsiang Huang, Khuong Vo, Ramses Seferino Trigo Torres, J. -C. Chiao, Juhyun Lee, Michael P. H. Lau, Xiaolei Xu, Hung Cao
Zebrafish is a powerful and widely-used model system for a host of biological investigations including cardiovascular studies and genetic screening.
However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels.
With the considerable development of customer-to-customer (C2C) e-commerce in the recent years, there is a big demand for an effective recommendation system that suggests suitable websites for users to sell their items with some specified needs.
In particular, when analyzing the applications of deep learning in sentiment analysis, we found that the current approaches are suffering from the following drawbacks: (i) the existing works have not paid much attention to the importance of different types of sentiment terms, which is an important concept in this area; and (ii) the loss function currently employed does not well reflect the degree of error of sentiment misclassification.