  Recently, a variety of model designs and methods have blossomed in the
context of the sentiment analysis domain. However, there is still a lack of
wide and comprehensive studies of aspect-based sentiment analysis (ABSA). We
want to fill this gap and propose a comparison with ablation analysis of aspect
term extraction using various text embedding methods. We particularly focused
on architectures based on long short-term memory (LSTM) with optional
conditional random field (CRF) enhancement using different pre-trained word
embeddings. Moreover, we analyzed the influence on the performance of extending
the word vectorization step with character embedding. The experimental results
on SemEval datasets revealed that not only does bi-directional long short-term
memory (BiLSTM) outperform regular LSTM, but also word embedding coverage and
its source highly affect aspect detection performance. An additional CRF layer
consistently improves the results as well.
