Information Technology and the success of its related services (blogs; forums; etc.) have paved the way for a massive mode of opinion expression on the most varied subjects (e-commerce websites; art reviews; etc). This abundance of opinions could appear as a real gold mine for internet users, but it can also be a source of indecision because available opinions may be ill-assorted if not contradictory. A reliable and relevant information management of opinions bases requires systems able to directly analyze the content of opinions expressed in natural language. It allows controlling subjectivity in evaluation process and avoiding smoothing effects of statistical treatments. Most of the so-called recommender systems are unable to manage all the semantic richness of a review and prefer to associate to the review an assessment system that supposes a substantial implication and specific competences of the internet user. Our aim is minimizing user intervention in the collaborative functioning of recommender systems thanks to an automated processing of available reviews in natural language by the recommender system itself. Our topic segmentation method extracts the subjects of interest from the,reviews; and then our sentiment analysis approach computes the opinion related to these criteria. These knowledge extraction methods are combined with multicriteria analysis techniques adapted to expert assessments fusion. This proposal should finally contribute to the coming of a new generation of more relevant; reliable and personalized recommender systems.
