  Fine-grained sentiment analysis is receiving increasing attention in recent
years. Extracting opinion target expressions (OTE) in reviews is often an
important step in fine-grained, aspect-based sentiment analysis. Retrieving
this information from user-generated text, however, can be difficult. Customer
reviews, for instance, are prone to contain misspelled words and are difficult
to process due to their domain-specific language. In this work, we investigate
whether character-level models can improve the performance for the
identification of opinion target expressions. We integrate information about
the character structure of a word into a sequence labeling system using
character-level word embeddings and show their positive impact on the system's
performance. Specifically, we obtain an increase by 3.3 points F1-score with
respect to our baseline model. In further experiments, we reveal encoded
character patterns of the learned embeddings and give a nuanced view of the
performance differences of both models.
