Aspect Based Sentiment Analysis (ABSA) aims at identifying the aspects of entities and the sentiment expressed towards each aspect. Substantial work already exists in English language and in domains where aspects are easy to define such as restaurants, hotels, laptops, etc. This paper investigates detection of aspects in French language and in the books reviews domain where expression is more complex and aspects are less easy to characterize. On the basis of a corpus that we annotated , 21 aspects were defined and categorized into eight main classes including a catch-all class, General, which was found to be absorbent. Several methods were carried out to address this difficulty, with varying efficiency: Random Forest and SVM provided better results than kNN and Neural Net. Combining these methods with voting rules helped to improve noticeably the results. On another side, the difficulty of the task and the limits of a lexical approach were further explored with a qualitative analysis of errors and a topological mapping of the data using Self Organising Maps.