  Analyzing textual data is a very challenging task because of the huge volume
of data generated daily. Fundamental issues in text analysis include the lack
of structure in document datasets, the need for various preprocessing steps
%(e.g., stem or lemma extraction, part-of-speech tagging, named entities
recognition...), and performance and scaling issues. Existing text analysis
architectures partly solve these issues, providing restrictive data schemas,
addressing only one aspect of text preprocessing and focusing on one single
task when dealing with performance optimization. %As a result, no definite
solution is currently available. Thus, we propose in this paper a new generic
text analysis architecture, where document structure is flexible, many
preprocessing techniques are integrated and textual datasets are indexed for
efficient access. We implement our conceptual architecture using both a
relational and a document-oriented database. Our experiments demonstrate the
feasibility of our approach and the superiority of the document-oriented
logical and physical implementation.
