Topic models extract groups of words from documents, whose interpretation as a topic
hopefully allows for a better understanding of the data. However, the resulting word groups …

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …

For many applications that require semantic understanding of short texts, inferring
discriminative and coherent latent topics from short texts is a critical and fundamental task …

Sentiment analysis is the computational study of people's opinions, sentiments, emotions,
moods, and attitudes. This fascinating problem offers numerous research challenges, but …

Learning topics from short texts has become a critical and fundamental task for
understanding the widely-spread streaming social messages, eg, tweets, snippets and …

With the expansion and acceptance of Word Wide Web, sentiment analysis has become
progressively popular research area in information retrieval and web data analysis. Due to …

The short text has been the prevalent format for information of Internet, especially with the
development of online social media. Although sophisticated signals delivered by the short …

Many applications require semantic understanding of short texts, and inferring discriminative
and coherent latent topics is a critical and fundamental task in these applications …

Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular
method for the task. However, unsupervised topic models often generate incoherent …

Topic modeling has been commonly used to discover topics from document collections.
However, unsupervised models can generate many incoherent topics. To address this …