  With the increase of online customer opinions in specialised websites and
social networks, the necessity of automatic systems to help to organise and
classify customer reviews by domain-specific aspect/categories and sentiment
polarity is more important than ever. Supervised approaches to Aspect Based
Sentiment Analysis obtain good results for the domain/language their are
trained on, but having manually labelled data for training supervised systems
for all domains and languages are usually very costly and time consuming. In
this work we describe W2VLDA, an almost unsupervised system based on topic
modelling, that combined with some other unsupervised methods and a minimal
configuration, performs aspect/category classifiation,
aspect-terms/opinion-words separation and sentiment polarity classification for
any given domain and language. We evaluate the performance of the aspect and
sentiment classification in the multilingual SemEval 2016 task 5 (ABSA)
dataset. We show competitive results for several languages (English, Spanish,
French and Dutch) and domains (hotels, restaurants, electronic-devices).
