In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space.
Topic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure.
Many recent works on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the corresponding opinion words for a given opinion target.
As one of the prevalent topic mining tools, neural topic modeling has attracted a lot of interests for the advantages of high efficiency in training and strong generalisation abilities.
Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics.
Most of the previous approaches model context and target words with RNN and attention.
Ranked #1 on Sentiment Analysis on Twitter
Label-specific topics can be widely used for supporting personality psychology, aspect-level sentiment analysis, and cross-domain sentiment classification.
This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents.