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Aspect Extraction with Bidirectional GRU and CRF

Opinion mining or sentiment analysis used to understand the community's opinions on a particular product. Sentiment analysis involves building the opinion collection and classification system. One of the most crucial tasks of sentiment analysis is the ability to extract aspects or features that opinions expressed on. There are many approaches and techniques used to explore these features from unstructured comments. We proposed a different approach to the above mentioned aspect extraction task in sentiment analysis using a deep learning model combining Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF). This model is trained on labeled data to extract and classify feature sets in comments. Our model uses a BiGRU neural network with word embeddings achieved by training GloVe on the SemEval 2014 dataset. The SemEval 2014 dataset include 7,686 reviews on two domains, Laptop and Restaurant. Experimental results showed that our aspect extraction model in sentiment analysis using BiGRU-CRF achieved significantly better accuracy than the state-of-the-art methods.