However, this approach has two main drawbacks: (i) the whole image usually contains more objects and backgrounds than the sentence itself; thus, matching them together will confuse the grounded model; (ii) CNN only extracts the features of the image but not the relationship between objects inside that, limiting the grounded model to learn complicated contexts.
Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries.
The objective of our research is to create a topic model that can achieve great performances on microtexts while requiring a small runtime for scalability to large datasets.
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.
Firstly, we introduce the Autoencoding Variational Inference for Aspect Discovery (AVIAD) model, which extends the previous work of Autoencoding Variational Inference for Topic Models (AVITM) to embed prior knowledge of seed words.
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.