A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution
Table 2: Summary of SemEval14 Task4 on aspect sentiment classification. #Sentence: number of sentences; #Aspect: number of aspects; #Positive, #Negative, and #Neutral: number of aspects with positive, negative and neutral opinions, respectively; #Sent. /w Asp.: number of sentences with at least one aspect that is associated with one of positive, negative or neutral opinion; #Contrastive Sent.: number of sentences with aspects associated with different opinions; %Contrastive Sent.: percentage of contrastive sentences in sentences with at least one aspect.
['[EMPTY]', '[BOLD] Laptop', '[BOLD] Restaurant']
We decompose these statistics to get deeper insights that may lead to a failed ASC classifier. We notice that although these datasets contain a moderate number of training sentences for laptop, sentences with at least one aspect (and thus has polarities of opinions) is less than 50%, as in #Sent. /w Asp.