With the rapid advancement of information technology, online information has been
exponentially growing day by day, especially in the form of text documents such as news …

Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

The recent advances in language modeling significantly improved the generative
capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

With the ever increasing social networking and online marketing sites, the reviews and blogs
obtained from those, act as an important source for further analysis and improved decision …

Today's text classifiers inevitably suffer from unintended dataset biases, especially the
document-level label bias and word-level keyword bias, which may hurt models' …

We introduce a novel approach for automatically classifying the sentiment of Twitter
messages. These messages are classified as either positive or negative with respect to a …

We study sources of investor disagreement using sentiment of investors from a social media
investing platform, combined with information on the users' investment approaches (eg …

We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections
of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in …

We consider the problem of classifying documents not by topic, but by overall sentiment, eg,
determining whether a review is positive or negative. Using movie reviews as data, we find …