Semantics is a difficult topic in NLP… Stanford has some great NLP tools that I suggest you take a look at, though they don’t have anything for semantic analysis as far as I can tell.

If this is for a large, ongoing project I implore you to write your own classifier! Though it would take a non-trivial amount of time and effort in the research and development it would be of great benefit to the NLP community!

If you take the above approach, you could use existing word-tagging libraries (Stanford has one for French), combined with something like Lexique’s French lexicon, and add your own semantic entries to the lexicon; then, train a Bayes classifier on your dataset. It’s not a perfect approach, but a good start in the right direction!

Edit You mention in your question that you don’t want to write your own classifier because your dataset isn’t large enough. I challenge that statement by telling you that a recent research project at my university trained a Bayes classifier to 75% accuracy to detect sarcasm with only 150 samples!

Almost all the current approaches in sentiment analysis require a lexicon to detect classes (positive/negative or more refined ones). And the big problem is: this lexicon has to be adapted to new corpora, so it’s extremely hard to find an existing classifier that will work on your data, because it won’t have the correct lexicon to handle it.

You’ll certainly have to work a little bit yourself if you want to obtain correct results.

Some ongoing competitions should deliver interesting approaches and resources in a close future: DEFT 2015. But it’s still a bit early to exploit them.

You can use FEEL: a French Expanded Emotion Lexicon.
http://advanse.lirmm.fr/feel.php
It contains more than 14 000 distinct French terms associated with either the conveyed polarity and the expressed emotion(s). It follows the Ekman emotional typology (Ekman, 1992) with 2 polarities and 6 basic emotions classes.

We have learned sentiment classification models on DEFT 2007 and DEFT 2015 benchamrks. We were ranked first, third and fifth on the different benchmarks.
We have made this models online on a dedicated web platform.
http://advanse.lirmm.fr:8081/sentiment-analysis-webpage/index
You just have to upload a file containing french text documents to obtains the expressed sentiments. You may also use the models locally by downloading a jar file.

Bonjour, Vous pouvez essayer Lettria. Les sentiments sont traite par calcul:
Par exemple: il est tres bon = 1.5
Il est bon = 0.5
il n est pas bon = -0.5
Plus d erreur de faux positifs ou de negation