The intent of this dissertation in computer science is to study
topic models for text analytics. The first objective of this
dissertation is to incorporate auxiliary information present in
text corpora to improve topic modelling for natural language
processing (NLP) applications. The second objective of this
dissertation is to extend existing topic models to employ
state-of-the-art nonparametric Bayesian techniques for better
modelling of text data. In particular, this dissertation focusses
on:
- incorporating hashtags, mentions, emoticons, and target-opinion
dependency present in tweets, together with an external sentiment
lexicon, to perform opinion mining or sentiment analysis on
products and services;
- leveraging abstracts, titles, authors, keywords, categorical
labels, and the citation network to perform bibliographic
analysis on research publications, using a supervised or
semi-supervised topic model; and
- employing the hierarchical Pitman-Yor process (HPYP) and the
Gaussian process (GP) to jointly model text, hashtags, authors,
and the follower network in tweets for corpora exploration and
summarisation.
In addition, we provide a framework for implementing arbitrary
HPYP topic models to ease the development of our proposed topic
models, made possible by modularising the Pitman-Yor processes.
Through extensive experiments and qualitative assessment, we find
that topic models fit better to the data as we utilise more
auxiliary information and by employing the Bayesian nonparametric
method
