Aspect Level Sentiment Classification with Deep Memory Network

Understand

We introduce a deep memory network for aspect level sentiment classification.

• Unlike feature-based SVM and sequential neural models such as LSTM, this approach explicitly captures the importance of each context word when inferring the sentiment polarity of an aspect.
• Such importance degree and text representation are calculated with multiple computational layers, each of which is a neural attention model over an external memory.
• Experiments on laptop and restaurant datasets demonstrate that our approach performs comparable to state-of-art feature based SVM system, and substantially better than LSTM and attention-based LSTM architectures.