Abstract
Aspect's extraction is a critical task in aspect-based sentiment analysis, including explicit and implicit aspects identification. While extensive research has identified explicit aspects, little effort has been put forward on implicit aspects extraction due to the complexity of the problem. Moreover, existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’ dependency problems. Therefore, in this paper, a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed. The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF (Bidirectional Long Short Memory-Conditional Random Field), which serve as a memory to process dependent sentences to infer implicit aspects. It can identify implicit aspects from four types of sentences, including independent and three types of dependent sentences. The study is evaluated on a large movie reviews dataset with 50k examples. The experimental results showed that the explicit aspect identification method achieved 89% F1-score and implicit aspect extraction methods achieved 76% F1-score. In addition, the proposed approach also performs better than the state-of-the-art techniques (NMFIAD and ML-KB+) on the product review dataset, where it achieved 93% precision, 92% recall, and 93% F1-score.
Highlights
In the last decade, the advancement of the internet encouraged people to participate in social media networks
The main focus is to develop an approach for mapping implicit aspects to explicit aspects
The identified explicit aspects serve as input for the aspect mapping algorithm
Summary
The advancement of the internet encouraged people to participate in social media networks. It allows the users to comment on a picture, video, or product they like or dislike Business giants use these reviews to extract sentiments that help them profile their customers and project their future business competition position. The state-of-the-art implicit aspect mapping techniques [22,23] are developed for product application domains. The product application domain only uses Type I implicit sentences [24] to identify relevant product aspects. This paper proposed a multi-level knowledge engineering approach to infer movie-related implicit aspects inspired by the product application domain. The proposed approach is capable of identifying the above four different types of dependent and independent sentences to identify movie-specific implicit aspects from the reviews. The proposed approach is validated on a large movie review dataset and compared with the state-of-the-art techniques on the product review dataset, discussed in the results section
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