Understanding Neural Networks through Representation Erasure
Table 6: Results for aspect rating classification (5-class) from different models.
['Aspect', 'service', 'location', 'rooms', 'value']
Since the sentiment correlation between any pair of aspects (and the overall score) is high, the result of which may confuse the model, we employ a strategy similar to that of \newcitelei2016rationalizing to pick less correlated examples. For a given aspect, we pick the 50,000 reviews for which the score of this aspect deviates the most from the mean of the other aspects. LSTM based models do not perform as competitively as simple bigram-based classification models in aspect classification tasks, which has also been observed in \newcitetang2016aspect.