Özet:
In this thesis, Recursive Deep Learning models have been implemented for Turkish sentiment
analysis. Although natural language processing has made progress recently, representing
compositional meanings is a challenging task. The traditional deep learning methods claim
sentences as an ordinary linear structure, i.e. chains or sequences. In this thesis, tree-structured
representations of the language have been developed to improve the compositional semantics
of the Turkish language considering the morphological structure of the words. To this end, a
novel Morphologically Enriched Turkish Sentiment Treebank (MS-TR) has been constructed
to encode sentences structure. MS-TR is the first fully-labelled sentiment analysis treebank,
which has four different annotation levels, including morph-level, stem-level, token-level, and
review-level. Recursive Neural Tensor Networks (RNTN), which operate over MS-TR, have
achieved much better results compared to the machine learning methods. In addition to the
RNTN model, an advanced tree-structured LSTM model (ACT-LSTM) has been proposed as
a novel recursive deep architecture. ACT-LSTM combines both attention and memory
mechanisms over recursive tree structures, which learn latent structural information while
learning more important parts of the sentences. ACT-LSTM has been compared with advanced
chain-structured models to decide which architecture is better. As a third main contribution, a
novel metaheuristic training algorithm has been proposed to overcome the vanishing and
exploding gradients (VEG) problem, which is usually observed while training models. An
enhanced ternary Bees Algorithm (BA-3+) has been implemented, which maintains low time
complexity for large dataset classification problems by considering only three individual
solutions in each iteration. The algorithm utilises the greedy selection strategy of the local
solutions with exploitative search, stabilises the problem of VEGs of the decision parameters
using SGD learning with singular value decomposition, and explores the random global
solution with explorative search. BA-3+ has achieved faster convergence, avoiding getting
trapped at local optima compared to the classical SGD training algorithm.
