Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations

Hajek, Petr; Barushka, Aliaksandr; Munk, Michal

Automated opinion mining of consumer reviews is becoming increasingly important due to the rising influence of reviews on online retail shopping. Existing approaches to automated opinion classification rely either on sentiment lexicons or supervised machine learning. Deep neural networks perform this classification task particularly well by utilizing dense document representation in terms of word embeddings. However, this representation model does not consider the sentiment polarity or sentiment intensity of the words. To overcome this problem, we propose a novel model of deep neural network with word-sentiment associations. This model produces richer document representation that incorporates both word context and word sentiment. Specifically, our model utilizes pre-trained word embeddings and lexicon-based sentiment indicators to provide inputs to a deep feed-forward neural network. To verify the effectiveness of the proposed model, a benchmark dataset of Amazon reviews is used. Our results strongly support integrated document representation, which shows that the proposed model outperforms other existing machine learning approaches to opinion mining of consumer reviews.