Abstract:
Sentiment analysis has become one of the most important tools in natural language processing, since it opens many possibilities to understand people’s opinions on different topics. Aspect-based sentiment analysis aims to take this a step further and find out, what exactly someone is talking about, and if he likes or dislikes it. Real world examples of perfect areas for this topic are the millions of available customer reviews in online shops.
There have been multiple approaches to tackle this problem, using machine learning, deep learning and neural networks. However, currently the number of labelled reviews for training classifiers is very small.
Therefore, we undertook multiple steps to research ways of improving ABSA performance on small datasets, by comparing recurrent and feed-forward neural networks and incorporating additional input data that was generated using different readily available NLP tools.
