Feature-based opinion extraction is a task related to opinion
mining and information extraction which consists of automatically extracting
feature-level representations of opinions from subjective texts.
In the last years, some researchers have proposed domain-independent
solutions to this task. Most of them identify the feature being reviewed
by a set of words from the text. Rather than that, we propose a domainadaptable
opinion extraction system based on feature taxonomies (a semantic
representation of the opinable parts and attributes of an object)
which extracts feature-level opinions and maps them into the taxonomy.
The opinions thus obtained can be easily aggregated for summarization
and visualization. In order to increase precision and recall of the extraction
system, we define a set of domain-specific resources which capture
valuable knowledge about how people express opinions on each feature
from the taxonomy for a given domain. These resources are automatically
induced from a set of annotated documents. The modular design
of our architecture allows building either domain-specific or domainindependent
opinion extraction systems. According to some experimental
results, using the domain-specific resources leads to far better precision
and recall, at the expense of some manual effort