W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis
This addresses the need for automatic classification of customer reviews by aspect and sentiment without extensive labeled data, though it is incremental as it builds on existing unsupervised methods.
The authors tackled the problem of costly manual labeling for aspect-based sentiment analysis across multiple domains and languages by developing W2VLDA, an almost unsupervised system based on topic modeling, which achieved competitive results on the multilingual SemEval 2016 dataset for languages like English, Spanish, French, and Dutch.
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).