  We proposed a~new accurate aspect extraction method that makes use of both
word and character-based embeddings. We have conducted experiments of various
models of aspect extraction using LSTM and BiLSTM including CRF enhancement on
five different pre-trained word embeddings extended with character embeddings.
The results revealed that BiLSTM outperforms regular LSTM, but also word
embedding coverage in train and test sets profoundly impacted aspect detection
performance. Moreover, the additional CRF layer consistently improves the
results across different models and text embeddings. Summing up, we obtained
state-of-the-art F-score results for SemEval Restaurants (85%) and Laptops
(80%).
