INSIGHT 1 at SemEval 2016 Task 5: Deep Learning for Multilingual Aspect based Sentiment Analysis
This paper describes our deep learning based approach to multilingual aspect based sentiment analysis as part of SemEval 2016 Task 5. We use a convolutional neural network (CNN) for both aspect extraction and aspect based sentiment analysis. We cast aspect extraction as a multi label classification problem, outputting probabilities over aspects parameterized by a threshold. To determine the sentiment towards an aspect, we concatenate an aspect vector with every word embedding and apply a convolution over it. Our constrained system (unconstrained for English) achieves competitive results across all languages and domains, placing first or second in 5 and 7 out of 11 language domain pairs for aspect category detection (slot 1) and sentiment polarity (slot 3) respectively, thereby demonstrating the viability of a deep learning based approach for multilingual aspect based sentiment analysis.
*SEMEVAL