Balancing between holistic and cumulative sentiment classification

Sentiment analysis is a fast-accelerating discipline that develops algorithms for knowledge discovery from opinionated content. The challenges however, when it comes to analyzing user reviews are plenty. Bad-quality, informal use of language and lack of labels, are only a few obstacles. Most importantly, users, consciously or subconsciously, use different approaches for expressing their opinion about a product or a service. Some of them go sentence by sentence mentioning some positive and negative aspects whereas others provide a mixed piece of text where the reader is supposed to see the big picture to understand the message. In this work, we propose a novel neural network that deals with both situations. Our method, by combining convolutional, recurrent and attention neural networks can extract rich linguistic patterns that reveal the user's sentiment towards the entity under review. We evaluate our method in nine datasets that represent both binary and multi-class classification tasks. Experimental evaluation indicates that our method outperforms well-established deep learning approaches. Our approach outperformed the competitive methods in 8 out of 9 cases.