A Neural Word Embeddings Approach for Multi-Domain Sentiment Analysis

Multi-domain sentiment analysis consists in estimating the polarity of a given text by exploiting domain-specific information. One of the main issues common to the approaches discussed in the literature is their poor capabilities of being applied on domains which are different from those used for building the opinion model. In this paper, we will present an approach exploiting the linguistic overlap between domains to build sentiment models supporting polarity inference for documents belonging to every domain. Word embeddings together with a deep learning architecture have been implemented into the NeuroSent tool for enabling the building of multi-domain sentiment model. The proposed technique is validated by following the Dranziera protocol in order to ease the repeatability of the experiments and the comparison of the results. The outcomes demonstrate the effectiveness of the proposed approach and also set a plausible starting point for future work.