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For aspect-level sentiment analysis, the important first step is to identify the aspects and their associated entities present in customer reviews. Aspects can be either explicit or implicit, where the identification of the latter is more difficult. For restaurant reviews, this difficulty is escalated due to the vast number of entities and aspects present in reviews. The problem of implicit aspect identification has been studied for customer reviews in different domains, including restaurant reviews. However, the existing work for implicit aspect identification in customer reviews has the limitation of choosing at most one implicit aspect for each sentence. Furthermore, they deal only with a limited set of aspects related to a particular domain, thus have not faced the problem of ambiguity that arises when an opinion word is used to describe different aspects. This paper presents a novel approach for implicit aspect detection, which overcomes these two limitations. Our approach yields an F1measure of 0.842 when applied for a set of restaurant reviews collected from Yelp.

Implicit Aspect Detection in Restaurant Reviews using Cooccurence of Words