  This thesis explores the ways by how people express their opinions on German
Twitter, examines current approaches to automatic mining of these feelings, and
proposes novel methods, which outperform state-of-the-art techniques. For this
purpose, I introduce a new corpus of German tweets that have been manually
annotated with sentiments, their targets and holders, as well as polar terms
and their contextual modifiers. Using these data, I explore four major areas of
sentiment research: (i) generation of sentiment lexicons, (ii) fine-grained
opinion mining, (iii) message-level polarity classification, and (iv)
discourse-aware sentiment analysis. In the first task, I compare three popular
groups of lexicon generation methods: dictionary-, corpus-, and
word-embedding-based ones, finding that dictionary-based systems generally
yield better lexicons than the last two groups. Apart from this, I propose a
linear projection algorithm, whose results surpass many existing automatic
lexicons. Afterwords, in the second task, I examine two common approaches to
automatic prediction of sentiments, sources, and targets: conditional random
fields and recurrent neural networks, obtaining higher scores with the former
model and improving these results even further by redefining the structure of
CRF graphs. When dealing with message-level polarity classification, I
juxtapose three major sentiment paradigms: lexicon-, machine-learning-, and
deep-learning-based systems, and try to unite the first and last of these
groups by introducing a bidirectional neural network with lexicon-based
attention. Finally, in order to make the new classifier aware of discourse
structure, I let it separately analyze the elementary discourse units of each
microblog and infer the overall polarity of a message from the scores of its
EDUs with the help of two new approaches: latent-marginalized CRFs and
Recursive Dirichlet Process.
