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With the increase of online customer opinions in specialised websites andsocial networks, the necessity of automatic systems to help to organise andclassify customer reviews by domain-specific aspect/categories and sentimentpolarity is more important than ever. Supervised approaches to Aspect BasedSentiment Analysis obtain good results for the domain/language their aretrained on, but having manually labelled data for training supervised systemsfor all domains and languages are usually very costly and time consuming. Inthis work we describe W2VLDA, an almost unsupervised system based on topicmodelling, that combined with some other unsupervised methods and a minimalconfiguration, performs aspect/category classifiation,aspect-terms/opinion-words separation and sentiment polarity classification forany given domain and language. We evaluate the performance of the aspect andsentiment 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).

W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis